Ghassan Gholmieh

Restoring lost cognitive function

A prosthetic device that functions in a biomimetic manner to replace information transmission between cortical brain regions is considered. In such a prosthesis, damaged CNS neurons is replaced with a biomimetic system comprised of silicon neurons. The replacement silicon neurons would have functional properties specific to those of the damaged neurons and would both receive as inputs and send as outputs electrical activity to regions of the brain with which the damaged region previously communicated. Thus, the class of prosthesis proposed is one that would replace the computational function of the damaged brain and restore the transmission of that computational result to other regions of the nervous system.

Custom-designed high-density conformal planar multielectrode arrays for brain slice electrophysiology

Multielectrode arrays have enabled electrophysiological experiments exploring spatio-temporal dynamics previously unattainable with single electrode recordings. The finite number of electrodes in planar MEAs (pMEAs), however, imposes a trade-off between the spatial resolution and the recording area. This limitation was circumvented in this paper through the custom design of experiment-specific tissue-conformal high-density pMEAs (cMEAs). Four configurations were presented as examples of cMEAs designed for specific stimulation and recording experiments in acute hippocampal slices. These cMEAs conformed in designs to the slice cytoarchitecture whereas their high-density provided high spatial resolution for selective stimulation of afferent pathways and current source density (CSD) analysis. The cMEAs have 50 or 60 microm center-to-center inter-electrode distances and were manufactured on glass substrates by photolithographically defining ITO leads, insulating them with silicon nitride and SU-8 2000 epoxy-based photoresist and coating the etched electrode tips with gold or platinum. The ability of these cMEAs to stimulate and record electrophysiological activity was demonstrated by recording monosynaptic, disynaptic, and trisynaptic field potentials. The conformal designs also facilitated the selection of the optimal electrode locations for stimulation of specific afferent pathways (Schaffer collaterals; medial versus lateral perforant path) and recording the corresponding responses. In addition, the high-density of the arrays enabled CSD analysis of laminar profiles obtained through sequential stimulation along the CA1 pyramidal tree.

A biosensor for detecting changes in cognitive processing based on nonlinear systems analysis.

A new type of biosensor, based on hippocampal slices cultured on multielectrode arrays, and using nonlinear systems analysis for the detection and classification of agents interfering with cognitive function is described. A new method for calculating first and second order kernel was applied for impulse input-spike output datasets and results are presented to show the reliability of the estimations of this parameter. We further decomposed second order kernels as a sum of nine exponentially decaying Laguerre base functions. The data indicate that the method also reliably estimates these nine parameters. Thus, the state of the system can now be described with a set of ten parameters (first order kernel plus nine coefficients of Laguerre base functions) that can be used for detection and classification purposes.

17β-estradiol potentiates field excitatory postsynaptic potentials within each subfield of the hippocampus with greatest potentiation of the associational/commissural afferents of CA3

We sought to determine the impact of 17beta-estradiol throughout the hippocampal trisynaptic pathway and to investigate the afferent fiber systems within CA1 and CA3 in detail. To achieve this objective, we utilized multielectrode arrays to simultaneously record the field excitatory postsynaptic potentials from the CA1, dentate gyrus, and CA3 of rat hippocampal slices in the presence or absence of 100 pM 17beta-estradiol. We confirmed our earlier findings in CA1, where 17beta-estradiol significantly increased field excitatory postsynaptic potentials amplitude (20%+/-3%) and slope (22%+/-7%). 17beta-Estradiol significantly potentiated the field excitatory postsynaptic potentials in dentate gyrus, amplitude (15%+/-4%) and slope (17%+/-5), and in CA3, amplitude (15%+/-4%) and slope (19%+/-5%). Using a high-density multielectrode array, we sought to determine the source of potentiation in CA1 and CA3 by determining the impact of 17beta-estradiol on the apical afferents and the basal afferents within CA1 and on the mossy fibers and the associational/commissural fibers within CA3. In CA1, 17beta-estradiol induced a modest increase in the amplitude (7%+/-2%) and slope (9%+/-3%) following apical stimulation with similar magnitude of increase following basal stimulation amplitude (10%+/-2%) and slope (12%+/-3%). In CA3, 17beta-estradiol augmented the mossy fiber amplitude (15%+/-3%) and slope (18%+/-6%) and the associational/commissural fiber amplitude (31%+/-13%) and slope (40%+/-15%). These results indicate that 17beta-estradiol potentiated synaptic transmission in each subfield of the hippocampal slice, with the greatest magnitude of potentiation at the associational/commissural fibers in CA3. 17beta-Estradiol regulation of CA3 responses provides a novel site of 17beta-estradiol action that corresponds to the density of estrogen receptors within the hippocampus. The implications of 17beta-estradiol potentiation of the field potential in each of the hippocampal subfields and in particular CA3 associational/commissural fibers for memory function and clinical assessment are discussed.

An efficient method for studying short-term plasticity with random impulse train stimuli

In this article, we introduce an efficient method that models quantitatively nonlinear dynamics associated with short-term plasticity (STP) in biological neural systems. It is based on the Voterra-Wiener modeling approach adapted for special stimulus/response datasets. The stimuli are random impulse trains (RITs) of fixed amplitude and Poisson distributed, variable interimpulse intervals. The class of stimuli, we use can be viewed as a hybrid between the paired impulse approach (variable interimpulse interval between two input impulses) and the fixed frequency approach (impulses repeated at fixed intervals, varying in frequency from one stimulus dataset to the next). The responses are sequences of population spike amplitudes of variable size and are assumed to be contemporaneous with the corresponding impulses in the RITs they are evoked by. The nonlinear dynamics of the mechanisms underlying STP are captured by kernels used to create compact STP models with predictive capabilities. Compared to similar methods in the literature, the method presented in this article provides a comprehensive model of STP with considerable improvement in prediction accuracy and requires shorter experimental data collection time.

Detection and classification of neurotoxins using a novel short-term plasticity quantification method

A tissue-based biosensor is described for screening chemical compounds that rapidly affect the nervous system. The proposed sensor is an extension of a previous work on cultured hippocampal slices [Biosens. Bioelectron. 16 (2001) 491]. The detection of the chemical compounds is based on a novel quantification method of short-term plasticity (STP) of the CA1 system in acute hippocampal slices, using random electrical impulse sequences as inputs and population spike (PS) amplitudes as outputs. STP is quantified by the first and the second order kernels using a variant of the Volterra modeling approach. This approach is more specific and time-efficient than the conventional paired pulse and fixed frequency train methods [J. Neurosci. Methods 2 (2002) 111]. Describing the functional state of the biosensor, the kernels changed accordingly as chemical compounds were added. The second order kernel was decomposed into nine Laguerre functions. The corresponding Laguerre coefficients along with the first order kernel were used as features for classification purposes. The biosensor was tested using picrotoxin (100 microM), trimethylopropane phosphate (10 microM), tetraethylammonium (4 mM), valproate (5 mM), carbachol (5 mM), DAP5 (25 microM), CNQX (3 microM), and DNQX (0.15, 1.5, 3, 5 and 10 microM). Each chemical compound gave a different feature profile corresponding to its pharmacological class. The first order kernel and the Laguerre coefficients formed the input to an artificial neural network (ANN) comprised of a single layer of perceptrons. The ANN was able to classify each tested compound into its respective class.

An algorithm for real-time extraction of population EPSP and population spike amplitudes from hippocampal field potential recordings

A new method is presented for extracting the amplitude of excitatory post synaptic potentials (EPSPs) and spikes in real time. It includes a low pass filter (LPF), a differentiator, a threshold function, and an intelligent integrator. It was applied to EPSP and population spike data recorded in the Dentate Gyrus and the CA1 hippocampus in vitro. The accuracy of the extraction algorithm was evaluated via the extraction normalized mean square error (eNMSE) and was found to be very high (eNMSE < 5%). The preservation of neuronal information was confirmed using the Volterra-Poisson modeling approach. Volterra-Poisson kernels were computed using amplitudes extracted with both proposed and traditional methods. The accuracy of the computed kernels and the resulting model was evaluated via the prediction normalized mean square error (pNMSE) and was found to be very high (pNMSE < 5%). The similarity between the kernels computed when the proposed method was used to extract the field potential amplitude and their counterparts when the traditional method was used to extract the field potential amplitude confirms the preservation of the neuronal dynamics. The proposed method represents a new class of real time field potential amplitude extraction algorithms with complexity that can be included in hardware implementations.

Nonlinear Dynamic Model of CA1 Short-Term Plasticity using Random Impulse Train Stimulation

A comprehensive, quantitative description of the nonlinear dynamic characteristics of the short-term plasticity (STP) in the CA1 hippocampal region is presented. It is based on the Volterra–Poisson modeling approach using random impulse train (RIT) stimuli. In vitro hippocampal slice preparations were used from adult rats. RIT stimuli were applied at the Schaffer collaterals and population spike responses were recorded at the CA1 cell body layer. The computed STP descriptors that capture the nonlinear dynamics of the underlying STP mechanisms were the Volterra–Poisson kernels. The kernels quantified the presence of facilitatory and inhibitory STP behavior in magnitude and duration. A third order Volterra–Poisson STP model was introduced that accurately predicted in-sample and out-of-sample system responses. The proposed model could also accurately predict impulse pair and short impulse train system responses.

Detecting CA1 short-term plasticity variations associated with changes in stimulus intensity and extracellular medium composition

Changes in STP characteristics in the CA1 hippocampal region have been studied using the Volterra-Poisson modeling approach. Random impulse trains stimuli were applied to the Schaffer collaterals of the CA1 hippocampus in vitro and population spike responses were recorded at the pyramidal cell body layer. The computed Volterra-Poisson kernels captured the nonlinear dynamics associated with the mechanisms underlying STP by identifying quantitatively the magnitude and the duration of facilitatory and depressive behavior. The variations in STP characteristics secondary to changes in the stimulus intensity level and the addition of chemicals to the in vitro preparation were reflected in the differences of the calculated Volterra-Poisson kernels. Increasing the stimulus intensity caused an increase in the value of the first-order kernel (mean of the population spike amplitude) and decreased the peak facilitation of the second-order kernel. Picrotoxin caused an increase in the mean of PS amplitude, an increase in peak facilitation value, and an increase in the magnitude of the depressive phase of the second-order kernel. Decreasing the extracellular calcium concentration caused a decrease in the value of the first-order kernel and increased the peak facilitation value of the second-order kernel. Finally, the ability of the Volterra-Poisson modeling approach to track STP changes was confirmed through the low normalized mean square error associated with the prediction of in-sample and out-of-sample responses.

Modeling nonlinear neural dynamics with Volterra-Poisson kernels

A nonparametric quantitative model is introduced that captures the nonlinear dynamic properties of neural systems using input/output data. It is based on the Volterra modeling approach adapted for point-process inputs and outputs. Using input/output data, a model is presented for the CAl region of the hippocampus. The model represents reliably the nonlinear dynamic mapping performed by CAI with high accuracy. Compared to traditional descriptors of nonlinear neural dynamics, the presented model provides a generalized, comprehensive view.