Modeling the nonlinear properties of the in vitro hippocampal perforant path-dentate system using multielectrode array technology.

A modeling approach to characterize the nonlinear dynamic transformations of the dentate gyrus of the hippocampus is presented and experimentally validated. The dentate gyrus is the first region of the hippocampus which receives and integrates sensory information via the perforant path. The perforant path is composed of two distinct pathways: 1) the lateral path and 2) the medial perforant path. The proposed approach examines and captures the short-term dynamic characteristics of these two pathways using a nonparametric, third-order Poisson-Volterra model. The nonlinear characteristics of the two pathways are represented by Poisson-Volterra kernels, which are quantitative descriptors of the nonlinear dynamic transformations. The kernels were computed with experimental data from in vitro hippocampal slices. The electrophysiological activity was measured with custom-made multielectrode arrays, which allowed selective stimulation with random impulse trains and simultaneous recordings of extracellular field potential activity. The results demonstrate that this mathematically rigorous approach is suitable for the multipathway complexity of the hippocampus and yields interpretable models that have excellent predictive capabilities. The resulting models not only accurately predict previously reported electrophysiological descriptors, such as paired pulses, but more important, can be used to predict the electrophysiological activity of dentate granule cells to arbitrary stimulation patterns at the perforant path.

Spontaneous stellate ganglion nerve activity and ventricular arrhythmia in a canine model of sudden death.

BACKGROUND: Little information is available on the temporal relationship between instantaneous sympathetic nerve activity and ventricular arrhythmia in ambulatory animals. OBJECTIVE: The purpose of this study was to determine if increased sympathetic nerve activity precedes the onset of ventricular arrhythmia. METHODS: Simultaneous continuous long-term recording of left stellate ganglion (LSG) nerve activity and electrocardiography was performed in eight dogs with nerve growth factor infusion to the LSG, atrioventricular block, and myocardial infarction (experimental group) and in six normal dogs (control group). RESULTS: LSG nerve activity included low-amplitude burst discharge activity (LABDA) and high-amplitude spike discharge activity (HASDA). Both LABDA and HASDA accelerated heart rate. In the experimental group, most ventricular tachycardia (86.3%) and sudden cardiac death were preceded within 15 seconds by either LABDA or HASDA. The closer to onset of ventricular tachycardia, the higher the nerve activity. The majority of HASDA was followed immediately by either ventricular arrhythmia (21%) or QRS morphology changes (65%). HASDA occurred in a circadian pattern. HASDA occurred twice as often in the experimental group than in the control group. Electrical stimulation of LSG increased transmural heterogeneity of repolarization (Tpeak-end intervals) and induced either ventricular tachycardia or fibrillation in the experimental group but not in the control group. Immunohistochemical studies revealed increased synaptogenesis and nerve sprouting in the LSG in the experimental group. CONCLUSION: Two distinct types of LSG nerve activity (HASDA and LABDA) are present in the LSG of ambulatory dogs. The majority of malignant ventricular arrhythmias are preceded by either HASDA or LABDA, with HASDA particularly arrhythmogenic.

Screening for the effects of antiepileptic drugs on short term plasticity using a time efficient bioassay.

Screening for changes in the short-term plasticity (STP) characteristics induced by antiepileptic drugs (AEDs) can be accelerated using a novel in vitro bioassay. The bioassay is based on the analysis of varying population spike (PS) amplitudes recorded in the CA1 region of the hippocampal slice in response to Poisson distributed random electrical stimuli. Three antiepileptic drugs (phenytoin 100 microM, carbamazepine 100 microM, and valproate 700 microM) were tested at maximal effective therapeutic concentrations. The data were analyzed using an advanced nonlinear approach that is more specific and time-efficient than the conventional paired pulse and fixed frequency train methods. STP was quantified by the first and the second order Volterra kernels. The first order kernel (k1) represented the mean PS amplitude while the second order kernel (k2) quantified the effect on the current PS amplitude of the interaction between the current stimulus impulse and each past stimulus impulse within a time (memory) window mu. The mean PS (k1 decreased by 15%, 10%, and 7% when phenytoin, carbamazepine, and valproate were added respectively. Phenytoin caused an increase in the k2 peak facilitation in the high frequency domain. Carbamazepine impaired frequency facilitation in the theta frequency range by causing a left shift in the second order kernel.

Improving bioassay sensitivity for neurotoxins detection using volterra based third order nonlinear analysis.

Based on a novel analytical method for analyzing short-term plasticity (STP) of the CA1 hippocampal region in vitro, a screening tool for the detection and classification of unknown chemical compounds affecting the nervous system was recently introduced [1], [2]. The recorded signal consisted of evoked population spike in response to Poisson distributed random train impulse stimuli. The developed analytical approach used the first order Volterra kernel and the Laguerre coefficients of the second order Volterra model as classification features [3]. The biosensor showed encouraging results, and was able to classify out of sample compounds correctly [2]. We have taken an exploratory step to investigate the advantage of introducing a third order model [4]. DAP5, an NMDA channel blocker, did not show major changes in the second order kernel and in its corresponding Laguerre coefficients. Data were reanalyzed using a third order model. DAP5 showed discernable changes in the third order kernel as well as in the some of the corresponding Laguerre coefficients. Hence, the third order Volterra based model has the potential to improve the sensitivity and the discriminatory power of the proposed bioassay.

Left stellate ganglion and vagal nerve activity and cardiac arrhythmias in ambulatory dogs with pacing-induced congestive heart failure.

OBJECTIVES: The purpose of this study was to determine the patterns of autonomic nerve activity in congestive heart failure (CHF). BACKGROUND: The relationship between autonomic nerve activity and cardiac arrhythmias in CHF is unclear. METHODS: We implanted radiotransmitters in 6 dogs for continuous (24/7) simultaneous monitoring of left stellate ganglion nerve activity (SGNA), vagal nerve activity (VNA), and electrocardiography before and after pacing-induced CHF. RESULTS: Congestive heart failure increased both SGNA and VNA. The SGNA but not VNA manifested a circadian variation pattern. There was extensive sinus node fibrosis. We analyzed 2,263 episodes of prolonged (>3 s) sinus pauses (PSP) and 1,420 long (>10 s) episodes of paroxysmal atrial tachycardia (PAT). Most (95.3%) PSP episodes occurred at night, and 56% were preceded by a short burst of SGNA that induced transient sinus tachycardia. Long PAT episodes were typically (83%) induced by simultaneous SGNA and VNA discharge, followed by VNA withdrawal. Premature ventricular contractions and ventricular tachycardia were preceded by elevated SGNA. CONCLUSIONS: The reduction of sympathovagal balance at night in ambulatory dogs was due to reduced sympathetic discharge rather than a net increase of vagal discharge. The tachybrady syndrome in CHF might be triggered by an intermittent short burst of SGNA that resulted in tachycardia and sinus node suppression. Simultaneous sympathovagal discharge is a cause of long PAT episodes. These data indicate that there is an association between the specific patterns of autonomic nerve discharges and cardiac arrhythmia during CHF.

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.

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.

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.

VLSI implementation of a nonlinear neuronal model: a "neural prosthesis" to restore hippocampal trisynaptic dynamics.

We are developing a biomimetic electronic neural prosthesis to replace regions of the hippocampal brain area that have been damaged by disease or insult. We have used the hippocampal slice preparation as the first step in developing such a prosthesis. The major intrinsic circuitry of the hippocampus consists of an excitatory cascade involving the dentate gyrus (DG), CA3, and CA1 subregions; this trisynaptic circuit can be maintained in a transverse slice preparation. Our demonstration of a neural prosthesis for the hippocampal slice involves: (i) surgically removing CA3 function from the trisynaptic circuit by transecting CA3 axons, (ii) replacing biological CA3 function with a hardware VLSI (very large scale integration) model of the nonlinear dynamics of CA3, and (iii) through a specially designed multi-site electrode array, transmitting DG output to the hardware device, and routing the hardware device output to the synaptic inputs of the CA1 subregion, thus by-passing the damaged CA3. Field EPSPs were recorded from the CA1 dendritic zone in intact slices and "hybrid" DG-VLSI-CA1 slices. Results show excellent agreement between data from intact slices and transected slices with the hardware-substituted CA3: propagation of temporal patterns of activity from DG-->VLSI-->CA1 reproduces that observed experimentally in the biological DG-->CA3-->CA1 circuit.

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.

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 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.