Reconstruction of post-synaptic potentials by reverse modeling of local field potentials.

OBJECTIVE: Among electrophysiological signals, local field potentials (LFPs) are extensively used to study brain activity, either in vivo or in vitro. LFPs are recorded with extracellular electrodes implanted in brain tissue. They reflect intermingled excitatory and inhibitory processes in neuronal assemblies. In cortical structures, LFPs mainly originate from the summation of post-synaptic potentials (PSPs), either excitatory (ePSPs) or inhibitory (iPSPs) generated at the level of pyramidal cells. The challenging issue, addressed in this paper, is to estimate, from a single extracellularly-recorded signal, both ePSP and iPSP components of the LFP. APPROACH: The proposed method is based on a model-based reverse engineering approach in which the measured LFP is fed into a physiologically-grounded neural mass model (mesoscopic level) to estimate the synaptic activity of a sub-population of pyramidal cells interacting with local GABAergic interneurons. MAIN RESULTS: The method was first validated using simulated LFPs for which excitatory and inhibitory components are known a priori and can thus serve as a ground truth. It was then evaluated on in vivo data (PTZ-induced seizures, rat; PTZ-induced excitability increase, mouse; epileptiform discharges, mouse) and on in clinico data (human seizures recorded with depth-EEG electrodes). SIGNIFICANCE: Under these various conditions, results showed that the proposed reverse engineering method provides a reliable estimation of the average excitatory and inhibitory post-synaptic potentials originating of the measured LFPs. They also indicated that the method allows for monitoring of the excitation/inhibition ratio. The method has potential for multiple applications in neuroscience, typically when a dynamical tracking of local excitability changes is required.

Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients.

Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures. In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode de-composition (NA-MEMD). Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1-600 Hz), ripple (80-250 Hz), and fast-ripple (250-600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. However, the network phase-synchrony started to increase toward seizure end and achieved its maximum level at seizure offset for both types of epilepsy. This result suggests that hyper-synchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.

Using Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery.

For the more than 15 million patients who have drug-resistant epilepsy, surgical resection of the region where seizure arise is often the only alternative therapy. However, the identification of this epileptogenic zone (EZ) is often imprecise. Generally, insufficient EZ identification and resection may cause seizures to continue and too much resection may lead to unnecessary neurological deficits. In this paper, an automatic high frequency oscillations (HFOs) detection method based on noise-assisted multivariate EMD (NA-MEMD) is proposed to improve the localization of the EZ for epilepsy patients. In this method, different detected HFO types such as fast-ripple (FR), ripple (R), and fast-ripple concurrent with ripple (FRandR) are utilized to investigate their clinical relevance in identifying EZ. The proposed method may significantly improve the precision by which pathological brain tissue can be identified.

EMD-Based, Mean-Phase Coherence Analysis to Assess Instantaneous Phase-Synchrony Dynamics in Epilepsy Patients.

In this paper, an adaptive, non-linear, analytical methodology is proposed in order to quantitatively evaluate the instantaneous phase-synchrony dynamics in epilepsy patients. A group of finite neuronal oscillators is extracted from a multichannel electrocorticographic (ECoG) data, using the empirical mode decomposition (EMD). The instantaneous phases of the extracted oscillators are measured using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. Finally, the dynamical evolution of phase-synchrony among the extracted neuronal oscillators within 1-600 Hz frequency range is assessed using eigenvalue decomposition. A different phasesynchrony dynamics was observed in two patients with frontal vs. temporal lobe epilepsy, as their seizures evolve. However, experimental results demonstrated a hypersynchrony level at seizure offset for both types of epilepsy during the ictal periods. This result suggests that hypersynchronization of the epileptic network may be a crucial, self-regulatory mechanism by which the brain terminate seizures.

Should stimulation parameters be individualized to stop seizures: Evidence in support of this approach.

OBJECTIVE: Deep-brain electrical stimulation (DBS) is a treatment modality being explored for many neurologic diseases and is a potentially potent means of disrupting the aberrant rhythms that arise during the epileptic seizures that afflict >1% of the population. However, current DBS protocols typically employed are formulated a priori and do not reflect the electrophysiologic dynamics within the brain as seizures arise, which may underlie their limited efficacy. This study investigates how the efficacy of DBS could be improved using endogenous dynamics to inform stimulation protocols. METHODS: Multisite brain dynamics within the circuit of Papez were calculated in a chronic rat limbic epilepsy model induced via lithium chloride/pilocarpine intraperitoneal injections. Stimulation/recording electrodes were placed in the CA3 region of the left and right hippocampi and the anteromedial nucleus of the left thalamus. Deconvolution of local field potentials using empirical mode decomposition (EMD) and phase synchrony analysis revealed multisite coherence as seizures approached natural termination that could not be detected with Fourier analysis. Multisite stimulation used charge-neutral biphasic square waves at frequencies observed during natural termination. RESULTS: Synchronization of electrical activity across sites occurred as both spontaneous and evoked seizures naturally terminated. Furthermore, the location and frequency of the synchrony varied between subjects but was stable in time within each animal. DBS protocols were significantly more effective at rapidly stopping seizures when the frequency and location of multisite stimulation reflected the endogenous synchrony dynamics observed in each subject as seizures naturally terminated. SIGNIFICANCE: These results strongly support the approach of tailoring DBS protocols to individual endogenous rhythms that may represent how brains naturally resolve epileptic seizures. This approach may significantly improve the overall efficacy of this potentially important therapy.

Computational and experimental analysis of TMS-induced electric field vectors critical to neuronal activation.

OBJECTIVE: Transcranial magnetic stimulation (TMS) represents a powerful technique to noninvasively modulate cortical neurophysiology in the brain. However, the relationship between the magnetic fields created by TMS coils and neuronal activation in the cortex is still not well-understood, making predictable cortical activation by TMS difficult to achieve. Our goal in this study was to investigate the relationship between induced electric fields and cortical activation measured by blood flow response. Particularly, we sought to discover the E-field characteristics that lead to cortical activation. APPROACH: Subject-specific finite element models (FEMs) of the head and brain were constructed for each of six subjects using magnetic resonance image scans. Positron emission tomography (PET) measured each subject's cortical response to image-guided robotically-positioned TMS to the primary motor cortex. FEM models that employed the given coil position, orientation, and stimulus intensity in experimental applications of TMS were used to calculate the electric field (E-field) vectors within a region of interest for each subject. TMS-induced E-fields were analyzed to better understand what vector components led to regional cerebral blood flow (CBF) responses recorded by PET. MAIN RESULTS: This study found that decomposing the E-field into orthogonal vector components based on the cortical surface geometry (and hence, cortical neuron directions) led to significant differences between the regions of cortex that were active and nonactive. Specifically, active regions had significantly higher E-field components in the normal inward direction (i.e., parallel to pyramidal neurons in the dendrite-to-axon orientation) and in the tangential direction (i.e., parallel to interneurons) at high gradient. In contrast, nonactive regions had higher E-field vectors in the outward normal direction suggesting inhibitory responses. SIGNIFICANCE: These results provide critical new understanding of the factors by which TMS induces cortical activation necessary for predictive and repeatable use of this noninvasive stimulation modality.

Electrical control of epilepsy.

Epilepsy afflicts approximately 1-2% of the world's population. The mainstay therapy for treating the chronic recurrent seizures that are emblematic of epilepsy are drugs that manipulate levels of neuronal excitability in the brain. However, approximately one-third of all epilepsy patients get little to no clinical relief from this therapeutic regimen. The use of electrical stimulation in many forms to treat drug-refractory epilepsy has grown markedly over the past few decades, with some devices and protocols being increasingly used as standard clinical treatment. This article seeks to review the fundamental modes of applying electrical stimulation-from the noninvasive to the nominally invasive to deep brain stimulation-for the control of seizures in epileptic patients. Therapeutic practices from the commonly deployed clinically to the experimental are discussed to provide an overview of the innovative neural engineering approaches being explored to treat this difficult disease.

PET-based confirmation of orientation sensitivity of TMS-induced cortical activation in humans.

BACKGROUND: Currently, it is difficult to predict precise regions of cortical activation in response to transcranial magnetic stimulation (TMS). Most analytical approaches focus on applied magnetic field strength in the target region as the primary factor, placing activation on the gyral crowns. However, imaging studies support M1 targets being typically located in the sulcal banks. OBJECTIVE/HYPOTHESIS: To more thoroughly investigate this inconsistency, we sought to determine whether neocortical surface orientation was a critical determinant of regional activation. METHODS: MR images were used to construct cortical and scalp surfaces for 18 subjects. The angle (θ) between the cortical surface normal and its nearest scalp normal for ~50,000 cortical points per subject was used to quantify cortical location (i.e., gyral vs. sulcal). TMS-induced activations of primary motor cortex (M1) were compared to brain activations recorded during a finger-tapping task using concurrent positron emission tomographic (PET) imaging. RESULTS: Brain activations were primarily sulcal for both the TMS and task activations (P < 0.001 for both) compared to the overall cortical surface orientation. Also, the location of maximal blood flow in response to either TMS or finger-tapping correlated well using the cortical surface orientation angle or distance to scalp (P < 0.001 for both) as criteria for comparison between different neocortical activation modalities. CONCLUSION: This study provides further evidence that a major factor in cortical activation using TMS is the orientation of the cortical surface with respect to the induced electric field. The results show that, despite the gyral crown of the cortex being subjected to a larger magnetic field magnitude, the sulcal bank of M1 had larger cerebral blood flow (CBF) responses during TMS.

Rapid onset of a kainate-induced mirror focus in rat hippocampus is mediated by contralateral AMPA receptors.

The development of an epileptic "mirror" focus in an area of the brain contralateral to the primary epileptic focus typically evolves over days in the experimental setting after status epilepticus or electrical kindling of the primary focal region. In contrast, we observed the rapid development of an apparent mirror focus in the contralateral hippocampus following microinjection of kainic acid (KA) in the ipsilateral hippocampus in rats. Using multisite intracranial recordings, local field potentials were recorded in anesthetized adult male rats using electrodes implanted in the CA3 region of both hippocampi and in the anteromedial nucleus of the thalamus. Epileptogenesis was induced by microinjection of KA in the ipsilateral CA3 region. Development of seizures was followed under three experimental perturbations to the contralateral hippocampus: (A) no treatment, (B) pre-treatment with microinjection of the AMPA/Kainate receptor antagonist CNQX, and (C) pre-treatment with microinjection of the selective kainate receptor antagonist UBP 301. Both control and UBP 301 groups had seizures preferentially originate in the contralateral hippocampus appearing within ten minutes of KA injection. In contrast, the CNQX group had seizures preferentially originate in the ipsilateral hippocampus. By tracking the order of seizure onset, the probability that a hippocampal seizure would propagate across commissural fibers prior to any thalamic seizure activity was significantly reduced in the CNQX group compared to control and UBP groups suggesting that the AMPA receptor mediated component responsible for mirror focus development was also necessary for the spread of ictal activity via the commissural fibers. Understanding how a complex circuit in the brain develops may be critical to uncovering ways of either disrupting its development or treating its effects. The rapid appearance of a contralateral mirror focus via AMPA receptors in a limbic epilepsy model might be the mechanism by which a putative long-term mirror focus is established in vivo and may also underlie how secondary generalization progresses in some cases.

Synchrony dynamics across brain structures in limbic epilepsy vary between initiation and termination phases of seizures.

Neuronal populations in the brain achieve levels of synchronous electrophysiological activity during both normal brain function and pathological states such as epileptic seizures. Understanding how the dynamics of neuronal oscillators in the brain evolve from normal to diseased states is a critical component toward decoding such complex behaviors. In this study, we sought to develop a more in-depth understanding of multisite dynamics underlying seizure evolution in limbic epilepsy by analyzing oscillators in recordings of local field potentials from three brain structures (bilateral hippocampi and anteromedial thalamus) in a kainic acid in vivo rat model of temporal lobe epilepsy extracted using the empirical mode decomposition (EMD) technique. EMD provides an adaptive nonlinear decomposition into a set of finite oscillatory components. Oscillator frequencies, power, and phase synchrony were assessed within and between sites as seizures evolved. Consistent patterns of low-frequency (~35 Hz) synchrony occurred transiently during early-stage ictogenesis between thalamus and both hippocampi; in contrast, higher frequency (~120 Hz) synchrony appeared between thalamus and focal hippocampus as seizures naturally terminated. These multi-site synchrony events may provide a key insight into how synchrony disruption via stimulation could be targeted as well as contribute to a better understanding of how brain synchrony evolves in epilepsy.

A study of multi-site brain dynamics during limbic seizures.

Neuronal populations in the brain achieve levels of synchronous electrophysiological activity as a consequence of both normal brain functions as well as during pathological states such as in epileptic seizures. Understanding the nature of this synchrony and the dynamics of neuronal oscillators in the brain is a critical component towards decoding such complex behaviors. We have sought to achieve a more in-depth understanding of the dynamics underlying the evolution of seizures in limbic epilepsy by analyzing recordings of local field potentials from three subcortical nuclei that are part of the circuit of Papez in a kainic acid rat model of temporal lobe epilepsy using the empirical mode decomposition technique. The empirical mode decomposition allows for an adaptive and nonlinear decomposition of the local field potentials into a series of finite oscillatory components. We calculated the frequencies, power, and measures of phase synchrony of these oscillatory components as seizures evolve in the brain and discovered patterns of phase synchrony that varies between the different stages of the seizures.

Optimal spacing of surface electrode arrays for brain-machine interface applications.

Brain-machine interfaces (BMIs) use signals recorded directly from the brain to control an external device, such as a computer cursor or a prosthetic limb. These control signals have been recorded from different levels of the brain, from field potentials at the scalp or cortical surface to single neuron action potentials. At present, the more invasive recordings have better signal quality, but also lower stability over time. Recently, subdural field potentials have been proposed as a stable, good quality source of control signals, with the potential for higher spatial and temporal bandwidth than EEG. Here we used finite element modeling in rats and humans and spatial spectral analysis in rats to compare the spatial resolution of signals recorded epidurally (outside the dura), with those recorded from subdural and scalp locations. Resolution of epidural and subdural signals was very similar in rats and somewhat less so in human models. Both were substantially better than signals recorded at the scalp. Resolution of epidural and subdural signals in humans was much more similar when the cerebrospinal fluid layer thickness was reduced. This suggests that the less invasive epidural recordings may yield signals of similar quality to subdural recordings, and hence may be more attractive as a source of control signals for BMIs.

Assessing instantaneous synchrony of nonlinear nonstationary oscillators in the brain.

Neuronal populations throughout the brain achieve levels of synchronous electrophysiological activity as a consequence of both normal brain function as well as during pathological states such as in epileptic seizures. Understanding this synchrony and being able to quantitatively assess the dynamics with which neuronal oscillators across the brain couple their activity is a critical component toward decoding such complex behavior. Commonly applied techniques to resolve relationships between oscillators typically make assumptions of linearity and stationarity that are likely not to be valid for complex neural signals. In this study, intracranial electroencephalographic activity was recorded bilaterally in both hippocampi and in anteromedial thalamus of rat under normal conditions and during hypersynchronous seizure activity induced by focal injection of the epileptogenic agent kainic acid. Nonlinear oscillators were first extracted using empirical mode decomposition. The technique of eigenvalue decomposition was used to assess global phase synchrony of the highest energy oscillators. The Hilbert analytical technique was then used to measure instantaneous phase synchrony of these oscillators as they evolved in time. To test the reliability of this method, we first applied it to a system of two coupled Rössler attractors under varying levels of coupling with small frequency mismatch. The application of these analytical techniques to intracranially recorded brain signals provides a means for assessing how complex oscillatory behavior in the brain evolves and changes during both normal activity and as a consequence of diseased states without making restrictive and possibly erroneous assumptions of the linearity and stationarity of the underlying oscillatory activity.

Modulation of instantaneous synchrony during seizures by deep brain stimulation.

Epileptic seizures were experimentally induced in the CA3 region of rat hippocampus in vivo. Recordings of seizure activity were made in both hippocampi as well as anteromedial region of the thalamus in order to analyze the instantaneous activity for synchronous oscillators. A new method is introduced for detecting this synchrony which combines empirical mode decomposition, the Hilbert analytic signal method and eigenvalue decomposition. Effects of targeted deep brain stimulation on multi-site synchrony were assessed as a means to extinguish hypersynchrony during epileptic seizures.

Neuronal desynchronization as a trigger for seizure generation.

Experimental reports have appeared which challenge the dogma that epileptic seizures arise as a consequence of neuronal hypersynchronization. We sought to explore what mechanisms that desynchronize neuronal firing could induce epileptic seizures. A computer model of connections in a mammalian hippocampal slice preparation was constructed including two recently-reported distinct inhibitory feedback circuits. When inhibition by interneurons that synapse on pyramidal dendrites was decreased, highly localized seizure-like bursting was observed in the CA3 region similar to that which occurs experimentally under GABAergic blockade. In contrast, when inhibition by interneurons that synapse in the axosomatic region was similarly decreased, no such bursting was observed. However, when this transient inhibition was increased, normal coordinated spread of excitation was interrupted by high-frequency localized seizure-like bursting. The increase of this inhibitory input resulted in decreased cell coupling of pyramidal neurons. A decrease in phase coherence was initially observed until seizure-like activity initiated causing a net increase in coherence as has been observed in epileptic patients. These results provide a possible pathway in which a decrease in synchronization could provide the trigger for inducing epileptiform activity.

Electrical control of epileptic seizures.

SUMMARY: Epilepsy is among the most common neurologic disorders, yet it is estimated that about one third of patients do not respond favorably to currently available drug treatments and up to 50% experience major side effects of these treatments. Although surgical resection of seizure foci can provide reduction or cessation of seizure incidents, a significant fraction of pharmacologically intractable seizure patients are not considered viable candidates for such procedures. Research advances in applying electrical stimulation as an alternative treatment for intractable epilepsy have been reported. The primary focus of these studies has been the search for optimized stimulation protocols by which to electrically suppress, revert or prevent seizures. In this review, the authors discuss some of the promising results that have been achieved. These results are organized in three broad categories based on how such protocols are generated. They focus on how information of the electrical activity in the brain is incorporated in the control schemes, namely: open loop, semiclosed loop, and closed loop protocols. Benefits, potential promises, and challenges of these different control techniques are discussed.

Manipulating epileptiform bursting in the rat hippocampus using chaos control and adaptive techniques.

Epilepsy is a relatively common disease, afflicting 1%-2% of the population, yet many epileptic patients are not sufficiently helped by current pharmacological therapies. Recent reports have suggested that chaos control techniques may be useful for electrically manipulating epileptiform bursting behavior in vitro and could possibly lead to an alternative method for preventing seizures. We implemented chaos control of spontaneous bursting in the rat hippocampal slice using robust control techniques: stable manifold placement (SMP) and an adaptive tracking (AT) algorithm designed to overcome nonstationarity. We examined the effect of several factors, including control radius size and synaptic plasticity, on control efficacy. AT improved control efficacy over basic SMP control, but relatively frequent stimulation was still necessary and very tight control was only achieved for brief stretches. A novel technique was developed for validating period-1 orbit detection in noisy systems by forcing the system directly onto the period-1 orbit. This forcing analysis suggested that period-1 orbits were indeed present but that control would be difficult because of high noise levels and nonstationarity. Noise might actually be lower in vivo, where regulatory inputs to the hippocampus are still intact. Thus, it may still be feasible to use chaos control algorithms for preventing epileptic seizures.

Identification of determinism in noisy neuronal systems.

Most neuronal ensembles are nonlinear excitable systems. Thus it is becoming common to apply principles derived from nonlinear dynamics to characterize neuronal systems. One important characterization is whether such systems contain deterministic behavior or are purely stochastic. Unfortunately, many methods used to make this distinction do not perform well when both determinism and high-amplitude noise are present which is often the case in physiological systems. Therefore, we propose two novel techniques for identifying determinism in experimental systems. The first, called short-time expansion analysis, examines the expansion rate of small groups of points in state space. The second, called state point forcing, derives from the technique of chaos control. The system state is forced onto a fixed point, and the subsequent behavior is analyzed. This technique can be used to verify the presence of fixed points (or unstable periodic orbits) and to assess stationarity. If these are present, it implies that the system contains determinism. We demonstrate the use and possible limitations of these two techniques in two systems: the Hénon map, a classic example of a chaotic system, and spontaneous epileptiform bursting in the rat hippocampal slice. Identifying the presence of determinism in a physiological system assists in the understanding of the system's dynamics and provides a mechanism for manipulating this behavior.