Complexity decline of hippocampal CA1 circuit model due to cholinergic deficiency associated with Alzheimer's disease.

A hallmark of Alzheimer's disease (AD) is cholinergic system dysfunction, directly affecting the hippocampal neurons. Previous experiments have demonstrated that reduced complexity is one significant effect of AD on electroencephalography (EEG). Motivated by these, this study explores reduced EEG complexity of cholinergic deficiency in AD by neurocomputation. We first construct a new hippocampal CA1 circuit model with cholinergic action. M-current IM and calcium-activated potassium current IAHP are newly introduced in the model to describe cholinergic input from the medial septum. Then, by enhancing IM and IAHP to mimic cholinergic deficiency, how cholinergic deficiency influences the model complexity is investigated by sample entropy (SampEn) and approximate entropy (ApEn). Numerical results show a more severe cholinergic deficit with lower model complexity. Furthermore, we conclude that the decline of SampEn and ApEn is due to the greatly diminished excitability of model neurons. These suggest that decreased neuronal excitability due to cholinergic impairment may contribute to reduced EEG complexity in AD. Subsequently, statistical analysis between simulated AD patients and normal control (NC) groups demonstrates that SampEn and auto-mutual-information (AMI) decrease rates significantly differ. Compared to NC, AD patients have a lower SampEn and a less negative AMI decline rate. These imply a low rate of new-generation information in AD brains with cholinergic deficits. Interestingly, the statistical correlation between SampEn and AMI is analyzed, and they have a large negative Pearson correlation coefficient. Thus, AMI reduction rates may be a complementary tool for complex analysis. Our modeling and complex analysis are expected to provide a deeper understanding of the reduced EEG complexity resulting from cholinergic deficiency.

Detecting stochastic multiresonance in neural networks via statistical complexity measure.

This paper employs statistical complexity measure (SCM) to investigate the occurrence of stochastic multiresonance (SMR) induced by noise and time delay in small-world neural networks coupled with FitzHugh-Nagumo (FHN) neurons. Our findings reveal that SCM exhibits four local maxima at four optimal noise levels, providing evidence for the occurrence of quadruple stochastic resonances. When time delay τ is taken into account in the information transmission, under moderate noise levels, SCM shows several local maxima when τ = n T e with n being a positive integer and T e being the period of subthreshold signal. This indicates the appearance of delay-induced SMR at the multiples of the period of subthreshold signal. Intriguingly, at low noise levels, a strong coherence between time delay and neuronal firing dynamics emerges at τ = n T e - 2 , as confirmed by a series of SCM maxima at these time delays. Furthermore, the study demonstrates that by adjusting the degrees and sizes of small-world networks, as well as the coupling strength, it is possible to optimize the strength of delay-induced SMR, thus maximizing the detection capability of subthreshold signal. The research results may provide us with an effective approach for understanding the role of time delay in signal detection and information transmission.

Decreased coherence in the model of the dorsal visual pathway associated with Alzheimer's disease.

Decreased coherence in electroencephalogram (EEG) has been reported in Alzheimer's disease (AD) experimentally, which could be considered as a typical electrophysiological characteristic in AD. This work aimed to investigate the effect of AD on coherence in the dorsal visual pathway by the technique of neurocomputation. Firstly, according to the hierarchical organization of the cerebral cortex and the information flows of the dorsal visual pathway, a more physiologically plausible neural mass model including cortical areas v1, v2, and v5 was established in the dorsal visual pathway. The three interconnected cortical areas were connected by ascending and descending projections. Next, the pathological condition of loss of long synaptic projections in AD was simulated by reducing the parameters of long synaptic projections in the model. Then, the loss of long synaptic projections on coherence among different visual cortex areas was explored by means of power spectral analysis and coherence function. The results demonstrate that the coherence between these interconnected cortical areas showed an obvious decline with the gradual decrease of long synaptic projections, i.e. decrease in descending projections from area v2 to v1 and v5 to v2 and ascending projection from area v2 to v5. Hopefully, the results of this study could provide theoretical guidance for understanding the dynamical mechanism of AD.

Control of epileptic activities in a cortex network of multiple coupled neural populations under electromagnetic induction.

Epilepsy is believed to be associated with the abnormal synchronous neuronal activity in the brain, which results from large groups or circuits of neurons. In this paper, we choose to focus on the temporal lobe epilepsy, and establish a cortex network of multiple coupled neural populations to explore the epileptic activities under electromagnetic induction. We demonstrate that the epileptic activities can be controlled and modulated by electromagnetic induction and coupling among regions. In certain regions, these two types of control are observed to show exactly reverse effects. The results show that the strong electromagnetic induction is conducive to eliminating the epileptic seizures. The coupling among regions has a conduction effect that the previous normal background activity of the region gives way to the epileptic discharge, owing to coupling with spike wave discharge regions. Overall, these results highlight the role of electromagnetic induction and coupling among the regions in controlling and modulating epileptic activities, and might provide novel insights into the treatments of epilepsy.

Effect of acetylcholine deficiency on neural oscillation in a brainstem-thalamus-cortex neurocomputational model related with Alzheimer's disease.

Previous works imply that involving brainstem in neuropathological studies of Alzheimer's disease (AD) is of clinically significant. This work constructs a comprehensive neural mass model for cholinergic neuropathogenesis that involves brainstem, thalamus and cortex, wherein how acetylcholine deficiency in AD affects neural oscillation of the model output is systematically explored from the perspective of neurocomputation. By decreasing synapse connectivity parameters in direct cholinergic pathway from brainstem to thalamus or in indirect glutamatergic synapse pathway from cortex to brainstem to mimic the pathological condition of reduced acetylcholine release in patients with AD, the property of neural oscillation in this model is numerically investigated by means of power spectrum in frequency domain and amplitude distribution in time domain. Simulated results demonstrate that decreasing synapse connectivity whether in the direct cholinergic pathway or in the indirect glutamatergic synapse pathway can alter the neural oscillation significantly in three aspects: it induces an obvious decrease of dominant frequency; it leads to a degraded rhythmic activity in the alpha frequency band as well as an enhanced rhythmic activity in the theta frequency band; it results in reduced oscillation amplitude of the model output. These results are agreement with the characteristic of electrophysiological EEG measurement recorded in AD, especially support the hypothesis that cholinergic deficiency is a promising pathophysiological origin of EEG slowing in AD. Our analysis indicates that targeting the cholinergic system may have potential prospects in early diagnosis and treatment of AD.

Classification of Alzheimer's disease progression based on sMRI using gray matter volume and lateralization index.

Note that identifying Mild Cognitive Impairment (MCI) is crucial to early detection and diagnosis of Alzheimer's disease (AD). This work explores how classification features and experimental algorithms influence classification performances on the ADNI database. Based on structural Magnetic Resonance Images (sMRI), two features including gray matter (GM) volume and lateralization index (LI) are firstly extracted through hypothesis testing. Afterward, several classifier algorithms including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor(KNN) and Support Vector Machine (SVM) with RBF kernel, Linear kernel or Polynomial kernel are established to realize binary classification among Normal Control (NC), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI) and AD groups. The main experimental results are as follows. (1) The classification performance in the feature of LI is poor compared with those in the feature of GM volume or the combined feature of LI and GM volume, i.e., the classification accuracies in the feature of LI are relatively low and unstable for most classifier models and subject groups. (2) Comparing with the classification performances in the feature of GM volume and the combined feature of LI and GM volume, the classification accuracy of NC group versus AD group is relatively stable for different classifier models, moreover, the accuracy of AD group versus NC group is almost the highest, with the most classification accuracy of 98.0909%. (3) For different subject groups, the SVM classifier algorithm with Polynomial kernel and the KNN classifier algorithm show relatively stable and high classification accuracy, while DT classifier algorithm shows relatively unstable and lower classification accuracy. (4) Except the groups of EMCI versus LMCI and NC versus EMCI, the classification accuracies are significantly enhanced by emerging the LI into the original feature of GM volume, with the maximum accuracy increase of 5.6364%. These results indicate that various factors of subject data, feature types and experimental algorithms influence classification performances remarkably, especially the newly introduced feature of LI into the feature of GM volume is helpful to improve classification results in some certain extent.

How glutamatergic synapse loss affects the firing rhythm of DG-CA3 model related with Alzheimer's disease.

As well known that synapse loss is a significant pathological feature of Alzheimer's disease (AD), meanwhile, the hippocampus is one of brain regions to be first affected in the early stage of AD. Thus, this work employs a comprehensive DG-CA3 network model of the hippocampus so as to explore the neuronal correlation between glutamatergic synapse loss and abnormal firing rhythm associated with AD from the perspective of neurocomputation. The neuropathological condition of glutamatergic synapse loss caused by the reduction of Shank3 protein in AD patients is imitated by decreasing glutamatergic excitatory synapse strength between different neurons. By means of power spectral analysis and dynamics technique, the numerical results reveal that excitability of pyramidal neuron as well as oriens lacunosum-moleculare (O-LM) cell in CA3 region is strongly degraded by the decrease of NMDA or AMPA-type glutamatergic excitatory synapse strength. Moreover, the relative power together with the peak of relative power density within alpha band is also diminished by decreasing glutamatergic synapse strength. These findings accord with the electrophysiological experiment of EEG that there is a decrease of alpha rhythm for AD patients, on the same time, they could highlight the significance of glutamatergic synapse loss in the pathogenesis of AD.

Controlling Alzheimer's Disease Through the Deep Brain Stimulation to Thalamic Relay Cells.

Experimental and clinical studies have shown that the technique of deep brain stimulation (DBS) plays a potential role in the regulation of Alzheimer's disease (AD), yet it still desires for ongoing studies including clinical trials, theoretical approach and action mechanism. In this work, we develop a modified thalamo-cortico-thalamic (TCT) model associated with AD to explore the therapeutic effects of DBS on AD from the perspective of neurocomputation. First, the neuropathological state of AD resulting from synapse loss is mimicked by decreasing the synaptic connectivity strength from the Inter-Neurons (IN) neuron population to the Thalamic Relay Cells (TRC) neuron population. Under such AD condition, a specific deep brain stimulation voltage is then implanted into the neural nucleus of TRC in this TCT model. The symptom of AD is found significantly relieved by means of power spectrum analysis and nonlinear dynamical analysis. Furthermore, the therapeutic effects of DBS on AD are systematically examined in different parameter space of DBS. The results demonstrate that the controlling effect of DBS on AD can be efficient by appropriately tuning the key parameters of DBS including amplitude A, period P and duration D. This work highlights the critical role of thalamus stimulation for brain disease, and provides a theoretical basis for future experimental and clinical studies in treating AD.

Predicting amplitude death with machine learning.

In nonlinear dynamics, a parameter drift can lead to a sudden and complete cessation of the oscillations of the state variables-the phenomenon of amplitude death. The underlying bifurcation is one at which the system settles into a steady state from chaotic or regular oscillations. As the normal functioning of many physical, biological, and physiological systems hinges on oscillations, amplitude death is undesired. To predict amplitude death in advance of its occurrence based solely on oscillatory time series collected while the system still functions normally is a challenge problem. We exploit machine learning to meet this challenge. In particular, we develop the scheme of "parameter-aware" reservoir computing, where training is conducted for a small number of bifurcation parameter values in the oscillatory regime to enable prediction upon a parameter drift into the regime of amplitude death. We demonstrate successful prediction of amplitude death for three prototypical dynamical systems in which the transition to death is preceded by either chaotic or regular oscillations. Because of the completely data-driven nature of the prediction framework, potential applications to real-world systems can be anticipated.

Characterizing dynamical transitions by statistical complexity measures based on ordinal pattern transition networks.

Complex network approaches have been recently emerging as novel and complementary concepts of nonlinear time series analysis that are able to unveil many features that are hidden to more traditional analysis methods. In this work, we focus on one particular approach: the application of ordinal pattern transition networks for characterizing time series data. More specifically, we generalize a traditional statistical complexity measure (SCM) based on permutation entropy by explicitly disclosing heterogeneous frequencies of ordinal pattern transitions. To demonstrate the usefulness of these generalized SCMs, we employ them to characterize different dynamical transitions in the logistic map as a paradigmatic model system, as well as real-world time series of fluid experiments and electrocardiogram recordings. The obtained results for both artificial and experimental data demonstrate that the consideration of transition frequencies between different ordinal patterns leads to dynamically meaningful estimates of SCMs, which provide prospective tools for the analysis of observational time series.

Tuning coupling rate to control oscillation quenching in fractional-order coupled oscillators.

Introducing the fractional-order derivative into the coupled dynamical systems intrigues gradually the researchers from diverse fields. In this work, taking Stuart-Landau and Van der Pol oscillators as examples, we compare the difference between fractional-order and integer-order derivatives and further analyze their influences on oscillation quenching behaviors. Through tuning the coupling rate, as an asymmetric parameter to achieve the change from scalar coupling to non-scalar coupling, we observe that the onset of fractional-order not only enlarges the range of oscillation death, but attributes to the transition from fake amplitude death to oscillation death for coupled Stuart-Landau oscillators. We go on to show that for a coupled Van der Pol system only in the presence of a fractional-order derivative, oscillation quenching behaviors will occur. The results pave a way for revealing the control mechanism of oscillation quenching, which is critical for further understanding the function of fractional-order in a coupled nonlinear model.

Dynamics analysis of the hippocampal neuronal model subjected to cholinergic action related with Alzheimer's disease.

There are evidences that the region of hippocampus is affected in the early stage of Alzheimer's disease (AD). Moreover, the hippocampal pyramidal neurons receive cholinergic input from the medial septum. Thus, this study, based on the results of electrophysiological experiments, first constructs a modified hippocampal CA1 pyramidal neuronal model by introducing two new currents of M-current and calcium ion-activated potassium ion current to depict the cholinergic input receiving from the medial septum, and then explores how acetylcholine deficiency and beta-amyloid accumulation under the pathological condition of AD influence the neuronal dynamics in terms of theta band power and spiking frequency using computational approach. By simulating acetylcholine potentiated M-current and calcium ion-activated potassium ion current, numerical results reveal that the relative theta band power increases significantly and the firing rate decreases obviously when acetylcholine is deficient. Similarly, by simulating beta-amyloid enhanced delay rectification potassium ion current, we also detect that the relative theta band power increases as well as the firing rate decreases remarkably as beta-amyloid is accumulated. In addition, the mechanism underlying these dynamical changes in theta rhythm and firing behavior is investigated by nonlinear behavioral analysis, which demonstrates that both deficiency in acetylcholine and accumulation in beta-amyloid can promote the emergence of stable equilibrium state in this modified hippocampal neuronal model. Note that acetylcholine deficiency together with beta-amyloid deposition plays key role in the pathogenesis of AD. We expect these findings could have important implications on better understanding pathogenesis and expounding potential biomarkers for AD.

Control analysis of electrical stimulation for epilepsy waveforms in a thalamocortical network.

Physiological experiments and computational models both show that the thalamic reticular nucleus (RE) participates in inducing various firing patterns of cortex. Absence seizure, featured by 2-4 Hz spike-wave discharges (SWD) oscillation, is a high incidence of disease in children. Lots of electrophysiological experiments have verified the correlation between absence seizures and RE, however, the dynamical mechanisms are not well understood. Based on previous Taylor model, we firstly study the effects of external input and self-inhibition of RE on epilepsy transition. We show that increasing external input and self-inhibition of RE can lead the system from epileptic state to normal state, and vice versa. Next, we explore two stimulus strategies added in RE and various transition behaviors can be induced, such as high saturated state to clonic. Meanwhile, as the intensity of stimulation increasing, they can not only suppress the SWD, but also produce tonic-clonic oscillation. Finally, the control of DBS on single neuron cluster and two neuron clusters are compared and we find stimulating RE and TC simultaneously is a superior mode to stimulate anyone of RE or TC. It is hoped that the results we obtained will have an enlightenment on clinical treatment.

Alpha rhythm slowing in a modified thalamo-cortico-thalamic model related with Alzheimer's disease.

A decrease in alpha band power is defined as a hallmark of electroencephalogram (EEG) in Alzheimer's disease (AD). This study devotes to understanding the neuronal correlates of alpha rhythm slowing associated with AD from the view of neurocomputation. Firstly, a modified computational model of thalamo-cortico-thalamic (TCT) circuitry is constructed by incorporating two important biologically plausible ingredients. One is the disinhibition property between different inhibitory interneurons in the cortical module. The other is the full relay function of thalamic relay nucleus (TCR) to the cortical module. Then, by decreasing synaptic connectivity parameters to mimic the neuropathological condition of synapse loss in AD, the correlation between neuronal synaptic behavior and abnormal alpha rhythm is simulated by means of power spectral analysis. The results indicate that these decreases of synaptic activity, i.e., not only the excitatory synaptic connections from TCR to fast inhibitory interneurons Cfte and from excitatory interneurons to pyramidal neurons Cpxe but also the inhibitory synaptic connections from fast inhibitory interneurons to slow inhibitory interneurons Clfi and from inhibitory interneurons to TCR Ctii, can significantly diminish the peak power density over the alpha band of the thalamic output, which implies that there is a slowing of alpha band. Furthermore, the underlying mechanism behind the alpha rhythmic changes is analyzed using nonlinear dynamical technique. The results reveal that decreases of Cfte, Cpxe, Clfi and Ctii can make the thalamic module transfer from a limit cycle mode to a point attractor mode, which may lead to the alpha rhythm slowing in the modified TCT model. We expect this work can be helpful in identifying early biomarkers of AD's EEG and understanding potential pathogenesis of AD.

Aging transition in mixed active and inactive fractional-order oscillators.

Recently, a large number of studies have concentrated on aging transition, but they have so far been restricted to coupled integer-order oscillators. Here, we report the first study of aging transition in mixed active and inactive fractional-order oscillators. It has been demonstrated that while the heterogeneity is caused by the distance parameter, both the coupling strength and the fractional-order derivative can modulate the critical ratio at which aging transition occurs. In addition, a small fractional-order derivative may ruin the ability of oscillation and, thus, reduce the critical ratio in globally coupled fractional-order Stuart-Landau oscillators. Remarkably, the larger the natural frequency is the more easily the aging transition occurs in coupled fractional-order oscillators. Further studies have shown that, being diverse from an integer-order Stuart-Landau oscillator, the natural frequency may induce a Hopf bifurcation in a fractional-order Stuart-Landau oscillator, accordingly, introducing a new heterogeneity in the coupled fractional-order Stuart-Landau oscillators. Therein, a counterintuitive phenomenon has been found that the critical ratio depends unmonotonously on the coupling strength, which implies that the coupled fractional-order Stuart-Landau oscillators possess the weakest robustness of oscillation at a certain level of coupling strength.

The complete chloroplast genome of Vaccinium fragile (Vacciniaceae), a shrub endemic to China.

Vaccinium fragile, an endemic species in China, is a plant of the family Vacciniaceae. It is an evergreen shrubby tree distributed in Sichuan, Guizhou, Yunnan, and Tibet province of China. The chloroplast (cp) genome of V. fragile is 169,480 bp in size containing 123 unique genes, including 8 rRNA genes, 38 tRNA genes, and 77 protein-coding genes (PCGs). Phylogenetic analysis exhibited that both V. macrocarpon and V. fragile were phylogenetically closer to Arbutus unedo than other taxa in this study.

Suppressing bursting synchronization in a modular neuronal network with synaptic plasticity.

Excessive synchronization of neurons in cerebral cortex is believed to play a crucial role in the emergence of neuropsychological disorders such as Parkinson's disease, epilepsy and essential tremor. This study, by constructing a modular neuronal network with modified Oja's learning rule, explores how to eliminate the pathological synchronized rhythm of interacted busting neurons numerically. When all neurons in the modular neuronal network are strongly synchronous within a specific range of coupling strength, the result reveals that synaptic plasticity with large learning rate can suppress bursting synchronization effectively. For the relative small learning rate not capable of suppressing synchronization, the technique of nonlinear delayed feedback control including differential feedback control and direct feedback control is further proposed to reduce the synchronized bursting state of coupled neurons. It is demonstrated that the two kinds of nonlinear feedback control can eliminate bursting synchronization significantly when the control parameters of feedback strength and feedback delay are appropriately tuned. For the former control technique, the control domain of effective synchronization suppression is similar to a semi-elliptical domain in the simulated parameter space of feedback strength and feedback delay, while for the latter one, the effective control domain is similar to a fan-shaped domain in the simulated parameter space.

How synaptic plasticity influences spike synchronization and its transitions in complex neuronal network.

There is evidence that synaptic plasticity is a vital feature of realistic neuronal systems. This study, describing synaptic plasticity by a modified Oja learning rule, focuses on the effect of synapse learning rate on spike synchronization and its relative transitions in a Newman-Watts small-world neuronal network. The individual dynamics of each neuron is modeled by a simple Rulkov map that produces spiking behavior. Numerical results have indicated that large coupling can lead to a spatiotemporally synchronous pattern of spiking neurons; in addition, this kind of spike synchronization can emerge intermittently by turning information transmission delay between coupled neurons. Interestingly, with the advent of synaptic plasticity, spike synchronization is gradually destroyed by increasing synapse learning rate; moreover, the phenomenon of intermittent synchronization transitions becomes less and less obvious and it even disappears for relative larger learning rate. Further simulations confirm that spike synchronization as well as synchronization transitions is largely independent of network size. Meanwhile, we detect that large shortcuts probability can facilitate spike synchronization, but it is disadvantageous for delay-induced synchronization transitions.

Explosive death of conjugate coupled Van der Pol oscillators on networks.

Explosive death phenomenon has been gradually gaining attention of researchers due to the research boom of explosive synchronization, and it has been observed recently for the identical or nonidentical coupled systems in all-to-all network. In this work, we investigate the emergence of explosive death in networked Van der Pol (VdP) oscillators with conjugate variables coupling. It is demonstrated that the network structures play a crucial role in identifying the types of explosive death behaviors. We also observe that the damping coefficient of the VdP system not only can determine whether the explosive death state is generated but also can adjust the forward transition point. We further show that the backward transition point is independent of the network topologies and the damping coefficient, which is well confirmed by theoretical analysis. Our results reveal the generality of explosive death phenomenon in different network topologies and are propitious to promote a better comprehension for the oscillation quenching behaviors.

Enhancing coherence via tuning coupling range in nonlocally coupled Stuart-Landau oscillators.

Nonlocal coupling, as an important connection topology among nonlinear oscillators, has attracted increasing attention recently with the research boom of chimera states. So far, most previous investigations have focused on nonlocally coupled systems interacted via similar variables. In this work, we report the evolutions of dynamical behaviors in the nonlocally coupled Stuart-Landau oscillators by applying conjugate variables feedback. Through rigorous analysis, we find that the oscillation death (OD) can convert into the amplitude death (AD) via the cluster state with the increasing of coupling range, making the AD regions to be expanded infinitely along two directions of both the natural frequency and the coupling strength. Moreover, the limit cycle oscillation (OS) region and the mixed region of OD and OS will turn to anti-synchronization state through amplitude-mediated chimera. Therefore, the procedure from local coupling to nonlocal one implies indeed the continuous enhancement of coherence among neighboring oscillators in coupled systems.