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Ghassan Gholmieh

Publications and source records attributed to Ghassan Gholmieh.

6 recordsLinked to original sources
PubMed2006

Circadian variations of stellate ganglion nerve activity in ambulatory dogs.

BACKGROUND: The presence of circadian variations in sympathetic outflow from the stellate ganglia is unclear. OBJECTIVES: The purpose of this study was to continuously record stellate ganglion nerve activity (SGNA) in ambulatory dogs. METHODS: We performed continuous 24-hour left (N = 3) or bilateral (N = 3) SGNA recordings in normal ambulatory dogs using implanted Data Sciences International transmitters. We also performed simultaneous ECG recording (n = 5) or simultaneous ECG and blood pressure recordings (n = 1). RESULTS: The total duration of continuous ambulatory recording averaged 41.5 +/- 16.6 days. Five dogs had persistent stable recording, and one dog developed hardware malfunction in week 3. SGNA was followed immediately (<1 second) by heart rate and blood pressure elevation and a reduced standard deviation of consecutive activation cycle length (SDNN) from 236 +/- 93 ms to 121 +/- 51 ms (P = 0.007). Heart rate correlated significantly with SGNA. When there was a sudden increase of SGNA, the sudden increase occurred bilaterally in 90% of the episodes. Both heart rate and SGNA showed statistically significant (P <.01) circadian variation. Nadolol (20 mg/day for 5 days) reduced average heart rate from 99 +/- 8 bpm at baseline to 88 +/- 9 bpm (N = 6, P = .001) but did not significantly alter SGNA. Immunohistochemical staining of the stellate ganglia showed tyrosine hydroxylase-positive ganglion cells and nerves at the recording site. CONCLUSION: There is a circadian variation in sympathetic outflow from canine stellate ganglia. Circadian variation of SGNA is an important cause of circadian variations of cardiac sympathetic tone.

Adrenergic beta-Antagonists↗
PubMed2005

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.

Afferent Pathways↗
PubMed2004

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.

Action Potentials↗
PubMed2003

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.

Action Potentials↗
PubMed2002

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.

Action Potentials↗