Demitre Serletis

Prospective study of awake craniotomy used routinely and nonselectively for supratentorial tumors

OBJECT The authors prospectively assessed the value of awake craniotomy used nonselectively in patients undergoing resection of supratentorial tumors. METHODS The demographic features, presenting symptoms, tumor location, histological diagnosis, outcomes, and complications were documented for 610 patients who underwent awake craniotomy for supratentorial tumor resection. Intraoperative brain mapping was used in 511 cases (83.8%). Mapping identified eloquent cortex in 115 patients (22.5%) and no eloquent cortex in 396 patients (77.5%). RESULTS Neurological deficits occurred in 89 patients (14.6%). In the subset of 511 patients in whom brain mapping was performed, 78 (15.3%) experienced postoperative neurological worsening. This phenomenon was more common in patients with preoperative neurological deficits or in those individuals in whom mapping successfully identified eloquent tissue. Twenty-five (4.9%) of the 511 patients suffered intraoperative seizures, and two of these individuals required intubation and induction of general anesthesia after generalized seizures occurred. Four (0.7%) of the 610 patients developed wound complications. Postoperative hematomas developed in seven patients (1.1%), four of whom urgently required a repeated craniotomy to allow evacuation of the clot. Two patients (0.3%) required readmission to the hospital soon after being discharged. There were three deaths (0.5%). CONCLUSIONS Awake craniotomy is safe, practical, and effective during resection of supratentorial lesions of diverse pathological range and location. It allows for intraoperative brain mapping that helps identify and protect functional cortex. It also avoids the complications inherent in the induction of general anesthesia. Awake craniotomy provides an excellent alternative to surgery of supratentorial brain lesions in patients in whom general anesthesia has been induced.

The 128-Channel Fully Differential Digital Integrated Neural Recording and Stimulation Interface

We present a fully differential 128-channel integrated neural interface. It consists of an array of 8 X 16 low-power low-noise signal-recording and generation circuits for electrical neural activity monitoring and stimulation, respectively. The recording channel has two stages of signal amplification and conditioning with and a fully differential 8-b column-parallel successive approximation (SAR) analog-to-digital converter (ADC). The total measured power consumption of each recording channel, including the SAR ADC, is 15.5 ¿W. The measured input-referred noise is 6.08 ¿ Vrms over a 5-kHz bandwidth, resulting in a noise efficiency factor of 5.6. The stimulation channel performs monophasic or biphasic voltage-mode stimulation, with a maximum stimulation current of 5 mA and a quiescent power dissipation of 51.5 ¿W. The design is implemented in 0.35-¿m complementary metal-oxide semiconductor technology with the channel pitch of 200 ¿m for a total die size of 3.4 mm × 2.5 mm and a total power consumption of 9.33 mW. The neural interface was validated in in vitro recording of a low-Mg2+/high-K+ epileptic seizure model in an intact hippocampus of a mouse.

256-Channel Neural Recording and Delta Compression Microsystem With 3D Electrodes

A 3D microsystem for multi-site penetrating extracellular neural recording from the brain is presented. A 16 times 16-channel neural recording interface integrated prototype fabricated in 0.35 mum CMOS occupies 3.5 mm times 4.5 mm area. Each recording channel dissipates 15 muW of power with input-referred noise of 7 muVrms over 5 kHz bandwidth. A switched-capacitor delta read-out data compression circuit trades recording accuracy for the output data rate. An array of 1.5 mm platinum-coated microelectrodes is bonded directly onto the die. Results of in vitro experimental recordings from intact mouse hippocampus validate the circuit design and the on-chip electrode bonding technology.

Nonlinear and Complex Dynamics in Real Systems

This paper was produced for the El-Naschie Symposium on Nonlinear Dynamics in Shanghai in December 2005. In this paper we provide a review of the literature with respect to fluctuations in real systems and chaos. In doing so, we contrast the order and organization hypothesis of real systems with nonlinear chaotic dynamics and discuss some techniques used in distinguishing between stochastic and deterministic behavior. Moreover, we look at the issue of where and when the ideas of chaos could profitably be applied to real systems.

System characterization of neuronal excitability in the hippocampus and its relevance to observed dynamics of spontaneous seizure-like transitions

Most forms of epilepsy are marked by seizure episodes that arise spontaneously. The low-magnesium/high-potassium (low-Mg(2+)/high-K(+)) experimental model of epilepsy is an acute model that produces spontaneous, recurring seizure-like events (SLEs). To elucidate the nature of spontaneous seizure transitions and their relationship to neuronal excitability, whole-cell recordings from the intact hippocampus were undertaken in vitro, and the response of hippocampal CA3 neurons to Gaussian white noise injection was obtained before and after treatment with various concentrations of low-Mg(2+)/high-K(+) solution. A second-order Volterra kernel model was estimated for each of the input-output response pairs. The spectral energy of the responses was also computed, providing a quantitative measure of neuronal excitability. Changes in duration and amplitude of the first-order kernel correlated positively with the spectral energy increase following treatment with low-Mg(2+)/high-K(+) solution, suggesting that variations in neuronal excitability are coded by the system kernels, in part by differences to the profile of the first-order kernel. In particular, kernel duration was more sensitive than amplitude to changes in spectral energy, and correlated more strongly with kernel area. An oscillator network model of the hippocampal CA3 was constructed to investigate the relationship of kernel duration to network excitability, and the model was able to generate spontaneous, recurrent SLEs by increasing the duration of a mode function analogous to the first-order kernel. Results from the model indicated that disruption to the dynamic balance of feedback was responsible for seizure-like transitions and the observed intermittency of SLEs. A physiological candidate for feedback imbalance consistent with the network model is the destabilizing interaction of extracellular potassium and paroxysmal neuronal activation. Altogether, these results (1) validate a mathematical model for epileptiform activity in the hippocampus by quantifying and subsequently correlating its behavior with an experimental, in vitro model of epilepsy; (2) elucidate a possible mechanism for epileptogenesis; and (3) pave the way for control studies in epilepsy utilizing the herein proposed experimental and mathematical setup.

Complexity in Neuronal Noise Depends on Network Interconnectivity

Abstract“Noise,” or noise-like activity (NLA), defines background electrical membrane potential fluctuations at the cellular level of the nervous system, comprising an important aspect of brain dynamics. Using whole-cell voltage recordings from fast-spiking stratum oriens interneurons and stratum pyramidale neurons located in the CA3 region of the intact mouse hippocampus, we applied complexity measures from dynamical systems theory (i.e., 1/fγ noise and correlation dimension) and found evidence for complexity in neuronal NLA, ranging from high- to low-complexity dynamics. Importantly, these high- and low-complexity signal features were largely dependent on gap junction and chemical synaptic transmission. Progressive neuronal isolation from the surrounding local network via gap junction blockade (abolishing gap junction-dependent spikelets) and then chemical synaptic blockade (abolishing excitatory and inhibitory post-synaptic potentials), or the reverse order of these treatments, resulted in emergence of high-complexity NLA dynamics. Restoring local network interconnectivity via blockade washout resulted in resolution to low-complexity behavior. These results suggest that the observed increase in background NLA complexity is the result of reduced network interconnectivity, thereby highlighting the potential importance of the NLA signal to the study of network state transitions arising in normal and abnormal brain dynamics (such as in epilepsy, for example).

Tailored Unilobar and Multilobar Resections for Orbitofrontal-Plus Epilepsy

BACKGROUND Surgery for frontal lobe epilepsy often has poor results, likely because of incomplete resection of the epileptogenic zone. OBJECTIVE To present our experience with a series of patients manifesting 2 different anatomo-electro-clinical patterns of refractory orbitofrontal epilepsy, necessitating different surgical approaches for resection in each group. METHODS Eleven patients with refractory epilepsy involving the orbitofrontal region were consecutively identified over 3 years in whom stereoelectroencephalography identified the epileptogenic zone. All patients underwent preoperative evaluation, stereoelectroencephalography, and postoperative magnetic resonance imaging. Demographic features, seizure semiology, imaging characteristics, location of the epileptogenic zone, surgical resection site, and pathological diagnosis were analyzed. Surgical outcome was correlated with type of resection. RESULTS Five patients exhibited orbitofrontal plus frontal epilepsy with the epileptogenic zone consistently residing in the frontal lobe; after surgery, 4 patients were free of disabling seizures (Engel I) and 1 patient improved (Engel II). The remaining 6 patients had multilobar epilepsy with the epileptogenic zone located in the orbitofrontal cortex associated with the temporal polar region (orbitofrontal plus temporal polar epilepsy). After surgery, all 6 patients were free of disabling seizures (Engel I). Pathology confirmed focal cortical dysplasia in all patients. We report no complications or mortalities in this series. CONCLUSION Our findings highlight the importance of differentiating between orbitofrontal plus frontal and orbitofrontal plus temporal polar epilepsy in patients afflicted with seizures involving the orbitofrontal cortex. For identified cases of orbitofrontal plus temporal polar epilepsy, a multilobar resection including the temporal pole may lead to improved postoperative outcomes with minimal morbidity or mortality.

VLSI multivariate phase synchronization epileptic seizure detector

A low-power VLSI seizure detector is presented. It combines a 256-channel analog neural recording chip and a low-power synthesized digital VLSI processor. The processor computes the bivariate phase synchronization on any two neural inputs from a set of 256 and their instantaneous magnitude. For experimentation with in vitro epilepsy models, a low-cost technique to implement on-chip gold microelectrodes was utilized. Results are shown using an in vitro low Mg2+ mouse epilepsy model and human EEG data.

Classification of Multiple Seizure-Like States in Three Different Rodent Models of Epileptogenesis

Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. This study focused on objective classification of the multiple states involved in the brains epileptiform activity. Four datasets from three different rodent hippocampal preparations were explored, wherein seizure-like-events (SLE) were induced by the perfusion of a low - Mg2+ /high-K+ solution or 4-Aminopyridine. Local field potentials were recorded from CA3 pyramidal neurons and interneurons and modeled as Markov processes. Specifically, hidden Markov models (HMM) were used to determine the nature of the states present. Properties of the Hilbert transform were used to construct the feature spaces for HMM training. By sequentially applying the HMM training algorithm, multiple states were identified both in episodes of SLE and nonSLE activity. Specifically, preSLE and postSLE states were differentiated and multiple inner SLE states were identified. This was accomplished using features extracted from the lower frequencies (1-4 Hz, 4-8 Hz) alongside those of both the low- (40-100 Hz) and high-gamma (100-200 Hz) of the recorded electrical activity. The learning paradigm of this HMM-based system eliminates the inherent bias associated with other learning algorithms that depend on predetermined state segmentation and renders it an appropriate candidate for SLE classification.

Markers of pathological excitability derived from principal dynamic modes of hippocampal neurons

Transformation of principal dynamic modes (PDMs) under epileptogenic conditions was investigated by computing the Volterra kernels in a rodent epilepsy model derived from a mouse whole hippocampal preparation, where epileptogenesis was induced by altering the concentrations of Mg(2 +) and K(+) of the perfusate for different levels of excitability. Both integrating and differentiating PDMs were present in the neuronal dynamics, and both of them increased in absolute magnitude for increased excitability levels. However, the integrating PDMs dominated at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs were shifted to higher ranges under epileptogenic conditions, from ripple activities (75-200 Hz) to fast ripple activities (200-500 Hz).