Mesoscopic neuron population modeling of normal/epileptic brain dynamics

A Pilot Investigation of Visual Pathways in Patients with Mild Traumatic Brain Injury

In this study, we examined visual processing within primary visual areas (V1) in normal and visually impaired individuals who exhibit significant visual symptomology due to sports-related mild traumatic brain injury (mTBI). Five spatial frequency stimuli were applied to the right, left and both eyes in order to assess the visual processing of patients with sports-related mild traumatic brain injuries who exhibited visual abnormalities, i.e., photophobia, blurriness, etc., and controls. The measurement of the left/right eye and binocular integration was accomplished via the quantification of the spectral power and visual event-related potentials. The principal results have shown that the power spectral density (PSD) measurements display a distinct loss in the alpha band-width range, which corresponded to more instances of medium-sized receptive field loss. Medium-size receptive field loss may correspond to parvocellular (p-cell) processing deprecation. Our major conclusion provides a new measurement, using PSD analysis to assess mTBI conditions from primary V1 areas. The statistical analysis demonstrated significant differences between the mTBI and control cohort in the Visual Evoked Potentials (VEP) amplitude responses and PSD measurements. Additionally, the PSD measurements were able to assess the improvement in the mTBI primary visual areas over time through rehabilitation.

Dynamic effect of electromagnetic induction on epileptic waveform

BACKGROUND

Electromagnetic induction has recently been considered as an important factor affecting the activity of neurons. However, as an important form of intervention in epilepsy treatment, few people have linked the two, especially the related dynamic mechanisms have not been explained clearly.

METHODS

Considering that electromagnetic induction has some brain area dependence, we proposed a modified two-compartment cortical thalamus model and set eight different key bifurcation parameters to study the transition mechanisms of epilepsy. We compared and analyzed the application and getting rid of memristors of single-compartment and coupled models. In particular, we plotted bifurcation diagrams to analyze the dynamic mechanisms behind abundant discharge activities, which mainly involved Hopf bifurcations (HB), fold of cycle bifurcations (LPC) and torus bifurcations (TR).

RESULTS

The results show that the coupled model can trigger more discharge states due to the driving effect between compartments. Moreover, the most remarkable finding of this study is that the memristor shows two sides. On the one hand, it may reduce tonic discharges. On the other hand, it may cause new pathological states.

CONCLUSIONS

The work explains the control effect of memristors on different brain regions and lays a theoretical foundation for future targeted therapy. Finally, it is hoped that our findings will provide new insights into the role of electromagnetic induction in absence seizures.

Deep-layer motif method for estimating information flow between EEG signals

Accurate identification for the information flow between epileptic seizure signals is the key to construct the directional epileptic brain network which can be used to localize epileptic focus. In this paper, our concern is on how to improve the direction identification of information flow and also investigate how it can be cut off or weakened. In view of this, we propose the deep-layer motif method. Based on the directional index (DI) estimation using permutation conditional mutual information, the effectiveness of the proposed deep-layer motif method is numerically assessed with the coupled mass neural model. Furthermore, we investigate the robustness of this method in considering the interference of autaptic coupling, time delay and short-term plasticity. Results show that compared to the simple 1-layer motif method, the 2nd- and 3rd-layer motif methods have the dominant enhancement effects for the direction identification. In particular, deep-layer motif method possesses good anti-jamming performance and good robustness in calculating DI. In addition, we investigate the effect of deep brain stimulation (DBS) on the information flow. It is found that this deep-layer motif method is still superior to the single-layer motif method in direction identification and is robust to weak DBS. However, the high-frequency strong DBS can effectively decrease the DI suggesting the weakened information flow. These results may give new insights into the seizure detection and control.

EEG power spectral density in locked-in and completely locked-in state patients: a longitudinal study

Persons with their eye closed and without any means of communication is said to be in a completely locked-in state (CLIS) while when they could still open their eyes actively or passively and have some means of communication are said to be in locked-in state (LIS). Two patients in CLIS without any means of communication, and one patient in the transition from LIS to CLIS with means of communication, who have Amyotrophic Lateral Sclerosis were followed at a regular interval for more than 1 year. During each visit, resting-state EEG was recorded before the brain-computer interface (BCI) based communication sessions. The resting-state EEG of the patients was analyzed to elucidate the evolution of their EEG spectrum over time with the disease's progression to provide future BCI-research with the relevant information to classify changes in EEG evolution. Comparison of power spectral density (PSD) of these patients revealed a significant difference in the PSD's of patients in CLIS without any means of communication and the patient in the transition from LIS to CLIS with means of communication. The EEG of patients without any means of communication is devoid of alpha, beta, and higher frequencies than the patient in transition who still had means of communication. The results show that the change in the EEG frequency spectrum may serve as an indicator of the communication ability of such patients.

Dynamic Transitions of Epilepsy Waveforms Induced by Astrocyte Dysfunction and Electrical Stimulation

Experimental studies have shown that astrocytes participate in epilepsy through inducing the release of glutamate. Meanwhile, considering the disinhibition circuit among inhibitory neuronal populations with different time scales and the feedforward inhibition connection from thalamic relay nucleus to cortical inhibitory neuronal population, here, we propose a modified thalamocortical field model to systematically investigate the mechanism of epilepsy. Firstly, our results show that rich firing activities can be induced by astrocyte dysfunction, including high or low saturated state, high- or low-frequency clonic, spike-wave discharge (SWD), and tonic. More importantly, with the enhancement of feedforward inhibition connection, SWD and tonic oscillations will disappear. In other words, all these pathological waveforms can be suppressed or eliminated. Then, we explore the control effects after different external stimulations applying to thalamic neuronal population. We find that single-pulse stimulation can not only suppress but also induce pathological firing patterns, such as SWD, tonic, and clonic oscillations. And we further verify that deep brain stimulation can control absence epilepsy by regulating the amplitude and pulse width of stimulation. In addition, based on our modified model, 3 : 2 coordinated reset stimulation strategies with different intensities are compared and a more effective and safer stimulation mode is proposed. Our conclusions are expected to give more theoretical insights into the treatment of epilepsy.

Seizure localization using EEG analytical signals

OBJECTIVE

Localization of epileptic seizures, usually characterized by abnormal hypersynchronous wave patterns from the cortex, remains elusive. We present a novel, robust method for automatic localization of seizures on the scalp from clinical electroencephalogram (EEG) data.

METHODS

Seizure patient EEG data was decomposed via the Hilbert Transform and processed through the following methodology: sorting the analytic amplitude (AA) in the time instance, locating the maximum amplitude within the vector of channels, cross-correlating amplitude values in the time index with the channel vector. The channel with highest AA value in time was located.

RESULTS

Our approach provides an automated way to isolate the epi-genesis of seizure events with 93.3% precision and 100% sensitivity. The method differentiates seizure-related neural activity from other common EEG noise artifacts (e.g., blinks, myogenic noise).

CONCLUSIONS

We evaluated performance characteristics of our source location methodology utilizing both phase and energy of EEG signals from patients who exhibited seizure events. Feasibility of the new algorithm is demonstrated and confirmed.

SIGNIFICANCE

The proposed method contributes to high-performance scalp localization for seizure events that is more straightforward and less computationally intensive than other methods (e.g., inverse source modeling). Ultimately, it may aid clinicians in providing improved patient diagnosis.

Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures

Epilepsy is one of the most common chronic neurological disorders, and therefore, diagnosis and treatment methods are urgently needed for these patients. Many methods and algorithms that can detect seizures in epileptic patients have been proposed. Electroencephalogram (EEG) is one of helpful tools for investigating epilepsy forms in patients, however, an expert in the neurological field must perform a visual inspection to identify a seizure. Such analyses require longer time because of the huge dataset recorded from many electrodes which are put on the human scalp. With the non-stationary nature of EEG, especially during the abnormality periods, entropy measures gain more interest in the field. In this work, by exploring the advantages of both reliable state-of-the-art entropies, fuzzy entropy and distribution entropy, a modified-Distribution entropy (mDistEn) for epilepsy detection is proposed. As the results, the proposed mDistEn method can successfully achieve the same consistency and better accuracy than using the state-of-the-art entropies. The mDistEn corresponds to higher Area Under the Curve (AUC) values compared with the fuzzy entropy and the distribution entropy and yields 92% classification accuracy.

Brain activity during time to contact estimation: an EEG study

Understanding the neural mechanisms associated with time to contact (TTC) estimation is an intriguing but challenging task. Despite the importance of TTC estimation in our everyday life, few studies have been conducted on it, and there are still a lot of unanswered questions and unknown aspects of this issue. In this study, we intended to address one of these unknown aspects. We used independent component analysis to systematically assess EEG substrates associated with TTC estimation using two experiments: (1) transversal motion experiment (when a moving object passes transversally in the frontoparallel plane from side to side in front of the observer), and (2) head-on motion experiment (when the observer is on the motion path of the moving object). We also studied the energy of all EEG sources in these two experiments. The results showed that brain regions involved in the transversal and head-on motion experiments were the same. However, the energy used by some brain regions in the head-on motion experiment, including some regions in left parietotemporal and left frontal lobes, was significantly higher than the energy used by those regions in the transversal motion experiment. These brain regions are dominantly associated with different kinds of visual attention, integration of visual information, and responding to visual motion.

Functional and effective connectivity based features of EEG signals for object recognition

Classifying different object categories is one of the most important aims of brain-computer interface researches. Recently, interactions between brain regions were studied using different methods, such as functional and effective connectivity techniques. Functional and effective connectivity techniques are applied to estimate human brain areas connectivity. The main purpose of this study is to compare classification accuracy of the most advanced functional and effective methods in order to classify 12 basic object categories using Electroencephalography (EEG) signals. In this paper, 19 channels EEG signals were collected from 10 healthy subjects; when they were visiting color images and instructed to select the target images among others. Correlation, magnitude square coherence, wavelet coherence (WC), phase synchronization and mutual information were applied to estimate functional cortical connectivity. On the other hand, directed transfer function, partial directed coherence, generalized partial directed coherence (GPDC) were used to obtain effective cortical connectivity. After feature extraction, the scalar feature selection methods including T-test and one-sided-anova were applied to rank and select the most informative features. The selected features were classified by a one-against-one support vector machine classifier. The results indicated that the use of different techniques led to different classifying accuracy and brain lobes analysis. WC and GPDC are the most accurate methods with performances of 80.15% and 64.43%, respectively.

Complex temporal patterns processing by a neural mass model of a cortical column

It is well known that neuronal networks are capable of transmitting complex spatiotemporal information in the form of precise sequences of neuronal discharges characterized by recurrent patterns. At the same time, the synchronized activity of large ensembles produces local field potentials that propagate through highly dynamic oscillatory waves, such that, at the whole brain scale, complex spatiotemporal dynamics of electroencephalographic (EEG) signals may be associated to sensorimotor decision making processes. Despite these experimental evidences, the link between highly temporally organized input patterns and EEG waves has not been studied in detail. Here, we use a neural mass model to investigate to what extent precise temporal information, carried by deterministic nonlinear attractor mappings, is filtered and transformed into fluctuations in phase, frequency and amplitude of oscillatory brain activity. The phase shift that we observe, when we drive the neural mass model with specific chaotic inputs, shows that the local field potential amplitude peak appears in less than one full cycle, thus allowing traveling waves to encode temporal information. After converting phase and amplitude changes obtained into point processes, we quantify input-output similarity following a threshold-filtering algorithm onto the amplitude wave peaks. Our analysis shows that the neural mass model has the capacity for gating the input signal and propagate selected temporal features of that signal. Finally, we discuss the effect of local excitatory/inhibitory balance on these results and how excitability in cortical columns, controlled by neuromodulatory innervation of the cerebral cortex, may contribute to set a fine tuning and gating of the information fed to the cortex.

Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis

Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.