Neural Network Embedding of Functional Microconnectome.

Our brain works as a vast and complex network system. We need to compress the networks to extract simple principles of network patterns and interpret these paradigms to better comprehend their complexities. This study treats this simplification process using a two-step analysis of topological patterns of functional connectivities that were produced from electrical activities of ~1000 neurons from acute slices of mouse brains [Kajiwara et al. 2021] As the first step, we trained an artificial neural network system called neural network embedding (NNE) and automatically compressed the functional connectivities. As the second step, we widely compared the compressed features with 15 representative network metrics, having clear interpretations, including not only common metrics, such as centralities clusters and modules but also newly developed network metrics. The result demonstrates not only the fact that the newly developed network metrics could complementarily explain the features of what was compressed by the NNE method but was previously relatively hard to explain using common metrics such as hubs, clusters and communities. This NNE method surpasses the limitations of commonly used human-made metrics but also provides the possibility that recognizing our own limitations drives us to extend interpretable targets by developing new network metrics.

Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation

Brain functions are sometimes emulated using some analog integrated circuits based on the organizational principle of natural neural networks. Neuromorphic engineering is the research branch devoted to the study and realization of such circuits with striking features. In this contribution, a novel small network of three neurons is introduced and investigated. The model is built from the coupling between two 2D Hindmarsh–Rose neurons through a 2D FitzHugh–Nagumo neuron. Thus, a heterogeneous coupled network is obtained. The biophysical energy released by the network during each electrical activity is evaluated. In addition, nonlinear analysis tools such as two-parameter Lyapunov exponent, bifurcation diagrams, the graph of the largest Lyapunov exponent, phase portraits, time series, as well as the basin of attractions are used to numerically investigate the network. It is found that the model can experience hysteresis justified by the simultaneous existence of three distinct electrical activities using the same set of parameters. Finally, the circuit implementation of the network is addressed in PSPICE to further support the obtained results.

Hamiltonian energy and coexistence of hidden firing patterns from bidirectional coupling between two different neurons

In this paper, bidirectional-coupled neurons through an asymmetric electrical synapse are investigated. These coupled neurons involve 2D Hindmarsh–Rose (HR) and 2D FitzHugh–Nagumo (FN) neurons. The equilibria of the coupled neurons model are investigated, and their stabilities have revealed that, for some values of the electrical synaptic weight, the model under consideration can display either self-excited or hidden firing patterns. In addition, the hidden coexistence of chaotic bursting with periodic spiking, chaotic spiking with period spiking, chaotic bursting with a resting pattern, and the coexistence of chaotic spiking with a resting pattern are also found for some sets of electrical synaptic coupling. For all the investigated phenomena, the Hamiltonian energy of the model is computed. It enables the estimation of the amount of energy released during the transition between the various electrical activities. Pspice simulations are carried out based on the analog circuit of the coupled neurons to support our numerical results. Finally, an STM32F407ZE microcontroller development board is exploited for the digital implementation of the proposed coupled neurons model.

Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation

Advancements in electrode technologies to both stimulate and record the central nervous system’s electrical activities are enabling significant improvements in both the understanding and treatment of different neurological diseases. However, the current neural recording and stimulating electrodes are metallic, requiring invasive and damaging methods to interface with neural tissue. These electrodes may also degrade, resulting in additional invasive procedures. Furthermore, metal electrodes may cause nerve damage due to their inherent rigidity. This paper demonstrates that novel electrically conductive organic fibers (ECFs) can be used for direct nerve stimulation. The ECFs were prepared using a standard polyester material as the structural base, with a carbon nanotube ink applied to the surface as the electrical conductor. We report on three experiments: the first one to characterize the conductive properties of the ECFs; the second one to investigate the fiber cytotoxic properties in vitro; and the third one to demonstrate the utility of the ECF for direct nerve stimulation in an in vivo rodent model.

Electroencephalography Patterns in Children with First Unprovoked Seizure

Background First unprovoked seizure (FUS) is a neurological health problem that occurs in an estimated 2% of children aged 16 years or younger. Electroencephalography (EEG) is an electrophysiological technique to record electrical activities arising from the brain; this technique can be used to evaluate patients with suspected seizures, epilepsy, and unusual concomitants. The objective of this study is to describe the EEG patterns in children with FUS and the factors associated with these EEG results. Method A retrospective analytic study was conducted in the Neuropaediatric Clinic, Dr Soetomo General Academic Hospital. The medical record data were obtained from January 2018 to December 2019. Children aged one month to 18 years with FUS and their complete EEG records were included. Descriptive statistics and the chi-square test with Cochran's Q test and Mantel–Haenszel tests were used for statistical calculations. Results One hundred participants enrolled the study. The majority (54%) showed abnormal EEG, which was dominated by epileptiform discharges (68.5%) consisting of benign epileptiform with centro-temporal spikes (BECTS), focal and generalized sharp waves, focal and generalized spikes, and EEG seizures. Factors associated with abnormal EEG results were children aged ≥ 5 years (p = 0.07, OR = 3.093, 95% CI = 1.361–7.030), focal seizure type (p = 0.021, OR = 6.286, 95% CI = 1.327–29,779), and long seizure duration ≥ 5 minutes (p < 0.001, OR = 8.333, 95% CI = 3.029–22.929). Conclusion Children with abnormal EEG were at risk for recurrent seizures. Over 50% of children with FUS had abnormal EEG results. In the present study, abnormal EEG results were frequently found in children with FUS, especially in older children (≥ 5 years old), those with focal seizures, and those with long seizure durations (≥ 5 minutes).

EEG-based human emotion recognition using entropy as a feature extraction measure

Many studies on brain–computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.

Update to the dataset of cerebral ischemia in juvenile pigs with evoked potentials

We expand from a spontaneous to an evoked potentials (EP) data set of brain electrical activities as electrocorticogram (ECoG) and electrothalamogram (EThG) in juvenile pig under various sedation, ischemia and recovery states. This EP data set includes three stimulation paradigms: auditory (AEP, 40 and 2000 Hz), sensory (SEP, left and right maxillary nerve) and high-frequency oscillations (HFO) SEP. This permits derivation of electroencephalogram (EEG) biomarkers of corticothalamic communication under these conditions. The data set is presented in full band sampled at 2000 Hz. We provide technical validation of the evoked responses for the states of sedation, ischemia and recovery. This extended data set now permits mutual inferences between spontaneous and evoked activities across the recorded modalities. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries.

Abundant Firing Patterns in a Memristive Morris–Lecar Neuron Model

Improving the neuron model and studying its electrical activities according to the real biophysical environment are significant in human cognitive brain activity and neural behavior. The complex transmembrane motion of ions on the neuronal cell membrane can establish time-varying electromagnetic fields and affect the transition firing patterns of neurons. In this paper, a threshold memristor is used to describe the electromagnetic induction and magnetic field effects of neuron cell membrane ion exchange to improve the neuron model, and a memristive Morris–Lecar (mM–L) neuron model is proposed. Numerical simulation confirms that different intensities of electromagnetic fields can produce distinct pattern transitions in electrical activities of the neuron, such as periodic bursting, periodic spiking, chaotic bursting. From the perspective of neuron’s interspike interval (ISI), the ISIs bifurcation in the multiparameter planes, ISIs firing periods, the variance of ISIs and other methods are used to find the trend of the mM–L neuron firing pattern transition. Finally, based on the 4D nonlinear differential equation of the mM–L neuron model, the complete electronic implementation of the model is designed. The output of the designed circuit is consistent with the theoretical prediction, which is extremely useful for studying the dynamics of a single neuron.

Reduced Cell Excitability of Cardiac Postganglionic Parasympathetic Neurons Correlates With Myocardial Infarction-Induced Fatal Ventricular Arrhythmias in Type 2 Diabetes Mellitus

ObjectiveWithdrawal of cardiac vagal activity is considered as one of the important triggers for acute myocardial infarction (MI)-induced ventricular arrhythmias in type 2 diabetes mellitus (T2DM). Our previous study demonstrated that cell excitability of cardiac parasympathetic postganglionic (CPP) neurons was reduced in T2DM rats. This study investigated whether cell excitability of CPP neurons is associated with cardiac vagal activity and MI-induced ventricular arrhythmias in T2DM rats.MethodsRat T2DM was induced by a high-fat diet plus streptozotocin injection. MI-evoked ventricular arrhythmia was achieved by surgical ligation of the left anterior descending coronary artery. Twenty-four-hour, continuous ECG recording was used to quantify ventricular arrhythmic events and heart rate variability (HRV) in conscious rats. The power spectral analysis of HRV was used to evaluate autonomic function. Cell excitability of CPP neurons was measured by the whole-cell patch-clamp technique.ResultsTwenty-four-hour ECG data demonstrated that MI-evoked fatal ventricular arrhythmias are more severe in T2DM rats than that in sham rats. In addition, the Kaplan-Meier analysis demonstrated that the survival rate over 2 weeks after MI is significantly lower in T2DM rats (15% in T2DM+MI) compared to sham rats (75% in sham+MI). The susceptibility to ventricular tachyarrhythmia elicited by programmed electrical stimulation was higher in anesthetized T2DM+MI rats than that in rats with MI or T2DM alone (7.0 ± 0.58 in T2DM+MI group vs. 3.5 ± 0.76 in sham+MI). Moreover, as an index for vagal control of ventricular function, changes of left ventricular systolic pressure (LVSP) and the maximum rate of increase of left ventricular pressure (LV dP/dtmax) in response to vagal efferent nerve stimulation were blunted in T2DM rats. Furthermore, T2DM increased heterogeneity of ventricular electrical activities and reduced cardiac parasympathetic activity and cell excitability of CPP neurons (current threshold-inducing action potentials being 62 ± 3.3 pA in T2DM rats without MI vs. 27 ± 1.9 pA in sham rats without MI). However, MI did not alter vagal control of the ventricular function and CPP neuronal excitability, although it also induced cardiac autonomic dysfunction and enhanced heterogeneity of ventricular electrical activities.ConclusionThe reduction of CPP neuron excitability is involved in decreased cardiac vagal function, including cardiac parasympathetic activity and vagal control of ventricular function, which is associated with MI-induced high mortality and malignant ventricular arrhythmias in T2DM.