Models of brainstem responses to bilateral electrical stimulation.
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ABSTRACT:
SUBMITTER: Colburn HS
PROVIDER: S-EPMC2644392 | biostudies-other | 2009 Mar
REPOSITORIES: biostudies-other
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Project description:Spinal cord injury (SCI) is a major cause of disability and pain, but little progress has been made in its clinical management. Low-frequency electrical stimulation (LFS) of various anti-nociceptive targets improves outcomes after SCI, including motor recovery and mechanical allodynia. However, the mechanisms of these beneficial effects are incompletely delineated and probably multiple. Our aim was to explore near-term effects of LFS in the hindbrain's nucleus raphe magnus (NRM) on cellular proliferation in a rat SCI model. Starting 24 h after incomplete contusional SCI at C5, intermittent LFS at 8 Hz was delivered wirelessly to NRM. Controls were given inactive stimulators. At 48 h, 5-bromodeoxyuridine (BrdU) was administered and, at 72 h, spinal cords were extracted and immunostained for various immune and neuroglial progenitor markers and BrdU at the level of the lesion and proximally and distally. LFS altered cell marker counts predominantly at the dorsal injury site. BrdU cell counts were decreased. Individually and in combination with BrdU, there were reductions in CD68 (monocytes) and Sox2 (immature neural precursors) and increases in Blbp (radial glia) expression. CD68-positive cells showed increased co-staining with iNOS. No differences in the expression of GFAP (glia) and NG2 (oligodendrocytes) or in GFAP cell morphology were found. In conclusion, our work shows that LFS of NRM in subacute SCI influences the proliferation of cell types implicated in inflammation and repair, thus providing mechanistic insight into deep brain stimulation as a neuromodulatory treatment for this devastating pathology.
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Project description:The auditory brainstem implant (ABI) can provide hearing sensation to individuals where the auditory nerve is damaged. However, patient outcomes with the ABI are typically much poorer than those for cochlear implant recipients. A major limitation to ABI outcomes is the number of implanted electrodes that can produce auditory responses to electric stimulation. One of the greatest challenges in ABI surgery is the intraoperative positioning of the electrode paddle, which must fit snugly within the cochlear nucleus complex. While there presently is no optimal procedure for intraoperative electrode positioning, intraoperative assessments may provide useful information regarding viable electrodes that may be included in patients' clinical speech processors. Currently, there is limited knowledge regarding the relationship between intraoperative data and post-operative outcomes. Furthermore, the relationship between initial ABI stimulation with and long-term perceptual outcomes is unknown. In this retrospective study, we reviewed intraoperative electrophysiological data from 24 ABI patients (16 adults and 8 children) obtained with two stimulation approaches that differed in terms of neural recruitment. The interoperative electrophysiological recordings were used to estimate the number of viable electrodes and were compared to the number of activated electrodes at initial clinical fitting. Regardless of the stimulation approach, the intraoperative estimate of viable electrodes greatly overestimated the number of active electrodes in the clinical map. The number of active electrodes was associated with long-term perceptual outcomes. Among patients with 10-year follow-up, at least 11/21 active electrodes were needed to support good word detection and closed-set recognition and 14/21 electrodes to support good open-set word and sentence recognition. Perceptual outcomes were better for children than for adults, despite a lower number of active electrodes.
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Project description:Electrical brain stimulation (EBS) is an appealing method to treat neurological disorders. To achieve optimal stimulation effects and a better understanding of the underlying brain mechanisms, neuroscientists have proposed computational modeling studies for a decade. Recently, multi-scale models that combine a volume conductor head model and multi-compartmental models of cortical neurons have been developed to predict stimulation effects on the macroscopic and microscopic levels more precisely. As the need for better computational models continues to increase, we overview here recent multi-scale modeling studies; we focused on approaches that coupled a simplified or high-resolution volume conductor head model and multi-compartmental models of cortical neurons, and constructed realistic fiber models using diffusion tensor imaging (DTI). Further implications for achieving better precision in estimating cellular responses are discussed.
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Project description:BACKGROUND:Cochlear implants (CIs) are neural prostheses that have been used routinely in the clinic over the past 25 years. They allow children who were born profoundly deaf, as well as adults affected by hearing loss for whom conventional hearing aids are insufficient, to attain a functional level of hearing. The "modern" CI (i.e., a multi-electrode implant using sequential coding strategies) has yielded good speech comprehension outcomes (recognition level for monosyllabic words about 50% to 60%, and sentence comprehension close to 90%). These good average results however hide a very important interindividual variability as scores in a given patients' population often vary from 5 to 95% in comparable testing conditions. Our aim was to develop a prognostic model for patients with unilateral CI. A novel method of objectively measuring electrical and neuronal interactions using electrical auditory brainstem responses (eABRs) is proposed. METHODS AND FINDINGS:The method consists of two measurements: 1) eABR measurements with stimulation by a single electrode at 70% of the dynamic range (four electrodes distributed within the cochlea were tested), followed by a summation of these four eABRs; 2) Measurement of a single eABR with stimulation from all four electrodes at 70% of the dynamic range. A comparison of the eABRs obtained by these two measurements, defined as the monaural interaction component (MIC), indicated electrical and neural interactions between the stimulation channels. Speech recognition performance without lip reading was measured for each patient using a logatome test (64 "vowel-consonant-vowel"; VCV; by forced choice of 1 out of 16). eABRs were measured in 16 CI patients (CIs with 20 electrodes, Digisonic SP; Oticon Medical ®, Vallauris, France). Significant correlations were found between speech recognition performance and the ratio of the amplitude of the V wave of the eABRs obtained with the two measurements (Pearson's linear regression model, parametric correlation: r2 = 0.26, p<0.05). CONCLUSIONS:This prognostic model allowed a substantial amount of the interindividual variance in speech recognition scores to be explained. The present study used measurements of electrical and neuronal interactions by eABR to assess patients' bio-electric capacity to use multiple information channels supplied by the implant. This type of prognostic information may be valuable in several ways. On the patient level, it allows customizing of individual treatments. ClinicalTrials.gov Identifier: NCT01805167.
| S-EPMC4858145 | biostudies-literature
Project description:BackgroundElectrical stimulation of the cortex using subdurally implanted electrodes can causally reveal structural connectivity by eliciting cortico-cortical evoked potentials (CCEPs). While many studies have demonstrated the potential value of CCEPs, the methods to evaluate them were often relatively subjective, did not consider potential artifacts, and did not lend themselves to systematic scientific investigations.New methodWe developed an automated and quantitative method called SIGNI (Stimulation-Induced Gamma-based Network Identification) to evaluate cortical population-level responses to electrical stimulation that minimizes the impact of electrical artifacts. We applied SIGNI to electrocorticographic (ECoG) data from eight human subjects who were implanted with a total of 978 subdural electrodes. Across the eight subjects, we delivered 92 trains of approximately 200 discrete electrical stimuli each (amplitude 4-15 mA) to a total of 64 electrode pairs.ResultsWe verified SIGNI's efficacy by demonstrating a relationship between the magnitude of evoked cortical activity and stimulation amplitude, as well as between the latency of evoked cortical activity and the distance from the stimulated locations.ConclusionsSIGNI reveals the timing and amplitude of cortical responses to electrical stimulation as well as the structural connectivity supporting these responses. With these properties, it enables exploration of new and important questions about the neurophysiology of cortical communication and may also be useful for pre-surgical planning.
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Project description:Repeated stress is one of the major risk factors for cerebrovascular disease, including stroke, and vascular dementia. However, the functional alterations in the cerebral hemodynamic response induced by chronic stress have not been clarified. Here, we investigated the in vivo cerebral hemodynamic changes and accompanying cellular and molecular changes in chronically stressed rats. After 3 weeks of restraint stress, the elicitation of stress was verified by behavioral despair in the forced swimming test and by physical indicators of stress. The evoked changes in the cerebral blood volume and pial artery responses following hindpaw electrical stimulation were measured using optical intrinsic signal imaging. We observed that, compared to the control group, animals under chronic restraint stress exhibited a decreased hemodynamic response, with a smaller pial arterial dilation in the somatosensory cortex during hindpaw electrical stimulation. The effect of chronic restraint stress on vasomodulator enzymes, including neuronal nitric oxide synthase (nNOS) and heme oxygenase-2 (HO-2), was assessed in the somatosensory cortex. Chronic restraint stress downregulated nNOS and HO-2 compared to the control group. In addition, we examined the subtypes of cells that can explain the environmental changes due to the decreased vasomodulators. The expression of parvalbumin in GABAergic interneurons and glutamate receptor-1 in neurons were decreased, whereas the microglial activation was increased. Our results suggest that the chronic stress-induced alterations in cerebral vascular function and the modulations of the cellular expression in the neuro-vasomodulatory system may be crucial contributing factors in the development of various vascular-induced conditions in the brain.
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Project description:Electrical stimulation of neural systems is a key tool for understanding neural dynamics and ultimately for developing clinical treatments. Many applications of electrical stimulation affect large populations of neurons. However, computational models of large networks of spiking neurons are inherently hard to simulate and analyze. We evaluate a reduced mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons which can be used to efficiently study the effects of electrical stimulation on large neural populations. The rich dynamical properties of this basic cortical model are described in detail and validated using large network simulations. Bifurcation diagrams reflecting the network's state reveal asynchronous up- and down-states, bistable regimes, and oscillatory regions corresponding to fast excitation-inhibition and slow excitation-adaptation feedback loops. The biophysical parameters of the AdEx neuron can be coupled to an electric field with realistic field strengths which then can be propagated up to the population description. We show how on the edge of bifurcation, direct electrical inputs cause network state transitions, such as turning on and off oscillations of the population rate. Oscillatory input can frequency-entrain and phase-lock endogenous oscillations. Relatively weak electric field strengths on the order of 1 V/m are able to produce these effects, indicating that field effects are strongly amplified in the network. The effects of time-varying external stimulation are well-predicted by the mean-field model, further underpinning the utility of low-dimensional neural mass models.
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Project description:Auditory brainstem responses (ABRs) are recorded in ten normal-hearing adults (20 ears) in response to a standard 100 micros click and five chirps having different durations (sweeping rates). The chirps are constructed from five versions of a power function model of the cochlear-neural delay that is based on derived-band ABR latencies from N=81 normal-hearing adults [Elberling, C., and Don, M. (2008). J. Acoust. Soc. Am. 124, 3022-3037]. The click and the chirps have identical amplitude spectra and, in general, for each of the three stimulus levels 60, 40, and 20 dB nHL, the ABRs to the chirps are significantly larger than the ABRs to the click. However, the shorter chirps are the most efficient at higher levels of stimulation whereas the longer chirps are the most efficient at lower levels. It is suggested that two different mechanisms are responsible for these observed changes with stimulus level--(1) upward spread of excitation at higher levels, and (2) an increased change of the cochlear-neural delay with frequency at lower levels.
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Project description:ProposeDirected cortical responses to intracranial electrical stimulation are a good standard for mapping inter-regional direct connectivity. Cortico-cortical evoked potential (CCEP), elicited by single pulse electrical stimulation (SPES), has been widely used to map the normal and abnormal brain effective network. However, automated processing of CCEP datasets and visualization of connectivity results remain challenging for researchers and clinicians. In this study, we develop a Matlab toolbox named MRIES (Mapping the Responses to Intracranial Electrical Stimulation) to automatically process CCEP data and visualize the connectivity results.MethodThe MRIES integrates the processing pipeline of the CCEP datasets and various methods for connectivity calculation based on low- and high-frequency signals with stimulation artifacts removed. The connectivity matrices are saved in different folders for visualization. Different visualization patterns (connectivity matrix, circle map, surface map, and volume map) are also integrated to the graphical user interface (GUI), which makes it easy to intuitively display and compare different connectivity measurements. Furthermore, one sample CCEP data set collected from eight epilepsy patients is used to validate the MRIES toolbox.ResultWe show the GUI and visualization functions of MRIES using one example CCEP data that has been described in a complete tutorial. We applied this toolbox to the sample CCEP data set to investigate the direct connectivity between the medial temporal lobe and the insular cortex. We find bidirectional connectivity between MTL and insular that are consistent with the findings of previous studies.ConclusionMRIES has a friendly GUI and integrates the full processing pipeline of CCEP data and various visualization methods. The MRIES toolbox, tutorial, and example data can be freely downloaded. As an open-source package, MRIES is expected to improve the reproducibility of CCEP findings and facilitate clinical translation.
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Project description:Peripheral neuromodulation has emerged as a powerful modality for controlling physiological functions and treating a variety of medical conditions including chronic pain and organ dysfunction. The underlying complexity of the nonlinear responses to electrical stimulation make it challenging to design precise and effective neuromodulation protocols. Computational models have thus become indispensable in advancing our understanding and control of neural responses to electrical stimulation. However, existing approaches suffer from computational bottlenecks, rendering them unsuitable for real-time applications, large-scale parameter sweeps, or sophisticated optimization. In this work, we introduce an approach for massively parallel estimation and optimization of neural fiber responses to electrical stimulation using machine learning techniques. By leveraging advances in high-performance computing and parallel programming, we present a surrogate fiber model that generates spatiotemporal responses to a wide variety of cuff-based electrical peripheral nerve stimulation protocols. We used our surrogate fiber model to design stimulation parameters for selective stimulation of pig and human vagus nerves. Our approach yields a several-orders-of-magnitude improvement in computational efficiency while retaining generality and high predictive accuracy, demonstrating its robustness and potential to enhance the design and optimization of peripheral neuromodulation therapies.
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