39 research outputs found

    Geometry based dynamic modeling of the neuron-electrode interface

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    A dynamic model of the neuron-electrode interface is presented which is based on the interface geometry and the electrical properties of the neuronal membrane. The model is used to compute the potential at the electrode and the local membrane potentials. Extracellular as well as intracellular current stimulation can be simulated. The results demonstrate that extracellular recorded action potentials with several shapes and amplitudes can be produced, depending on the properties of the interface and the membrane. With homogeneous membrane properties, only small amplitudes are simulated, High amplitudes are produced with decreased concentration of voltage sensitive channels in the lower membrane. Resemblance of the shape of the intracellular potential is accomplished by decreasing the capacity of the lower membran

    Triple leads with longitudinal guarded cathodes in spinal cord stimulation-effect of transversal lead separation

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    In spinal cord stimulation (SCS) clinical practice, longitudinal guarded cathode stimulation by a single lead, placed on the spinal cord midline provides the broadest parasthesia coverage. This study uses a triple lead longitudinal tripole with the center lead placed on the midline. The transversal spacing between the leads is varied to study its effect on the usage range (UR) and the recruited area (both depth and width) of dorsal colums activation

    Finite element modeling of the neuron-electrode interface: stimulus transfer and geometry

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    The relation between stimulus transfer and the geometry of the neuron-electrode interface can not be determined properly using electrical equivalent circuits, since current that flows from the sealing gap through the neuronal membrane is difficult to model in these circuits. Therefore, finite element modeling is proposed as a tool for linking the electrical properties of the neuron-electrode interface to its geometr

    The effect of training of culture neuronal networks, can they learn

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    Dissociated 1 or 2 days old postnatal rat cortical cells were cultured onto multi electrode arrays (MEA’s) with 61 electrode sites. They were trained with two protocols, i.e. the tetanic stimulation method from the report by Jimbo et al. (1998) and the selective adaptation protocol (report Shahaf and Marom, 2001). Tetanic stimulation training changed the net- work response significiantly. But training had no lasting effect, which means no learning result. The selective adaptation proto- col did also not lead to lasting learning effects

    Differentiating nociceptive mechanisms using electrocutaneous detection thresholds

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    Chronic pain after surgery is a frequent problem and difficult to treat. Persisting and chronic pain can be the result of the malfunctioning of nociceptive mechanisms; both ascending and descending pathways can, individually, attribute to chronic pain development. However, existing methodology does not discriminate between ascending and descending mechanisms. Here, we present psychophysical methods which could help improve the understanding of nociceptive malfunction in persistent post-surgical pain

    Analysis Of Nociceptive Evoked Potentials During Multi-Stimulus Experiments Using Linear Mixed Models

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    Neural processing of sensory stimuli can be studied using EEG by estimation of the evoked potential using the averages of large sets of trials. However, it is not always possible to include all stimulus parameters in a conventional analysis, since this would lead to an insufficient amount of trials to obtain the evoked potential by averaging. Linear mixed models use dependencies within the data to combine information from all data for the estimation of the evoked potential. In this work, it is shown that in multi-stimulus EEG data the quality of an evoked potential estimate can be improved by using a linear mixed model. Furthermore, the linear mixed model effectively deals with correlation between parameters in the data and reveals the influence of individual stimulus parameters

    Cortical processing of electrocutaneous stimuli in chronic stroke patients: a relationship with post-stroke shoulder pain.

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    Cerebral stroke is often associated with changes in cognitive-evaluative and somatosensory functions which may play a role in the development and maintenance of post-stroke pain

    Somatosensory and nociceptive changes in chronic post-stroke shoulder pain

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    Preliminary results from a cross-sectional study that investigated the relation between the presence of post-stroke shoulder pain and somatosensory and nociceptive changes are presented. The main finding is that both abnormal somatosensation and nociception are more frequently observed in stroke patients with pain as compared to pain-free stroke patients and healthy controls

    Computational modeling of Adelta-fiber-mediated nociceptive detection of electrocutaneous stimulation

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    Sensitization is an example of malfunctioning of the nociceptive pathway in either the peripheral or central nervous system. Using quantitative sensory testing, one can only infer sensitization, but not determine the defective subsystem. The states of the subsystems may be characterized using computational modeling together with experimental data. Here, we develop a neurophysiologically plausible model replicating experimental observations from a psychophysical human subject study. We study the effects of single temporal stimulus parameters on detection thresholds corresponding to a 0.5 detection probability. To model peripheral activation and central processing, we adapt a stochastic drift-diffusion model and a probabilistic hazard model to our experimental setting without reaction times. We retain six lumped parameters in both models characterizing peripheral and central mechanisms. Both models have similar psychophysical functions, but the hazard model is computationally more efficient. The model-based effects of temporal stimulus parameters on detection thresholds are consistent with those from human subject data
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