Arrhythmia as a Result of Poor Intercellular Coupling in the Sinus Node: A Simulation Study

Östborn, Per; Wohlfart, Björn; Öhlén, Gunnar

doi:10.1006/jtbi.2001.2339

Cited by 14 publications

(6 citation statements)

References 48 publications

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“…The mechanism by which Ca i is no longer associated with V m in a consistent and reliable fashion during multiple wavefront VF remains speculative, but several possibilities exist. At a general level, theoretical studies have shown examples in which feedback between two or more coupled excitable systems induces unstable chaotic behavior (7,14,21). Wavebreak promoted by Ca-induced Ca release could be an example of this, because the AP and Ca-induced Ca release are coupled excitable systems each capable of exhibiting complex dynamics.…”

Section: Discussionmentioning

confidence: 99%

Intracellular Ca dynamics in ventricular fibrillation

Omichi

Lamp

Lin

et al. 2004

American Journal of Physiology-Heart and Circulatory Physiology

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In the heart, membrane voltage (Vm) and intracellular Ca (Cai) are bidirectionally coupled, so that ionic membrane currents regulate Cai cycling and Cai affects ionic currents regulating action potential duration (APD). Although Cai reliably and consistently tracks Vm at normal heart rates, it is possible that at very rapid rates, sarcoplasmic reticulum Cai cycling may exhibit intrinsic dynamics. Non-voltage-gated Cai release might cause local alternations in APD and refractoriness that influence wavebreak during ventricular fibrillation (VF). In this study, we tested this hypothesis by examining the extent to which Cai is associated with Vm during VF. Cai transients were mapped optically in isolated arterially perfused swine right ventricles using the fluorescent dye rhod 2 AM while intracellular membrane potential was simultaneously recorded either locally with a microelectrode (5 preparations) or globally with the voltage-sensitive dye RH-237 (5 preparations). Mutual information (MI) is a quantitative statistical measure of the extent to which knowledge of one variable (Vm) predicts the value of a second variable (Cai). MI was high during pacing and ventricular tachycardia (VT; 1.13 +/- 0.21 and 1.69 +/- 0.18, respectively) but fell dramatically during VF (0.28 +/- 0.06, P < 0.001). Cai at sites 4-6 mm apart also showed decreased MI during VF (0.63 +/- 0.13) compared with pacing (1.59 +/- 0.34, P < 0.001) or VT (2.05 +/- 0.67, P < 0.001). Spatially, Cai waves usually bore no relationship to membrane depolarization waves during nonreentrant fractionated waves typical of VF, whereas they tracked each other closely during pacing and VT. The dominant frequencies of Vm and Cai signals analyzed by fast Fourier transform were similar during VT but differed significantly during VF. Cai is closely associated with Vm closely during pacing and VT but not during VF. These findings suggest that during VF, non-voltage-gated Cai release events occur and may influence wavebreak by altering Vm and APD locally.

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Section: Discussionmentioning

confidence: 99%

Intracellular Ca dynamics in ventricular fibrillation

Omichi

Lamp

Lin

et al. 2004

American Journal of Physiology-Heart and Circulatory Physiology

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“…First, increased fibrosis within the SAN may facilitate induction and maintenance of reentry around the SAN. 30, 39 Second, modeling 40 and experimental 41 studies have shown that increased fibrosis within the SAN may facilitate reentry circling through the SAN via two separate conduction pathways. Third, atrial pauses due to SAN depression may unmask ectopic activity from atrial latent pacemakers, that could compete with SAN pacemakers and trigger AF.…”

Section: Discussionmentioning

confidence: 99%

Upregulation of Adenosine A1 Receptors Facilitates Sinoatrial Node Dysfunction in Chronic Canine Heart Failure by Exacerbating Nodal Conduction Abnormalities Revealed by Novel Dual-Sided Intramural Optical Mapping

Lou¹,

Hansen²,

Fedorenko³

et al. 2014

Circulation

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Background While sinoatrial node (SAN) dysfunction is a hallmark of human heart failure (HF), the underlying mechanisms remain poorly understood. We aimed to examine the role of adenosine in SAN dysfunction and tachy-brady arrhythmias in chronic HF. Methods and Results We applied multiple approaches to characterize SAN structure, function and adenosine A1 receptor (A1R) expression in control (n=17) and four month tachypacing-induced chronic HF (n=18) dogs. Novel intramural optical mapping of coronary-perfused right atrial preparations revealed that adenosine (10μM) markedly prolonged post-pacing SAN conduction time in HF by 206±99ms (vs. 66±21ms in control, p=0.02). Adenosine induced SAN intra-nodal conduction block and/or micro-reentry in 6/8 HF vs. 0/7 controls (p=0.007). Adenosine-induced SAN conduction abnormalities and automaticity depression caused post-pacing atrial pauses in HF vs. control (17.1±28.9s vs. 1.5±1.3s, p<0.001). Furthermore, 10μM adenosine shortened atrial repolarization and led to pacing-induced atrial fibrillation (AF) in 6/7 HF vs. 0/7 control (p=0.002). Adenosine-induced SAN dysfunction and AF were abolished/prevented by A1R antagonists (50μM Theophylline/1μM DPCPX). A1R protein expression was significantly upregulated during HF in the SAN (by 47±19%) and surrounding atrial myocardium (by 90±40%). Interstitial fibrosis was significantly increased within the SAN in HF vs. control (38±4% vs. 23±4%, p<0.001). Conclusions In chronic HF, A1R upregulation in SAN pacemaker and atrial cardiomyocytes may increase cardiac sensitivity to adenosine. This effect may exacerbate conduction abnormalities in the structurally impaired SAN leading to SAN dysfunction, and potentiate atrial repolarization shortening thereby facilitating AF. AF may further depress SAN function and lead to tachy-brady arrhythmias in HF.

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“…As parallel to our neuronal networks, we can consider the case of the AV and SA nodes of the heart, where pacemaker cells are weakly coupled to themselves and surrounding tissue, but increased coupling can create enormous load, decreasing excitability. Decreased coupling of these cells provides less excitation to downstream cardiac regions, also leading to conduction failure [38, 39]. A similar interpretation can potentially be applied in future studies of neuronal gap junction coupling.…”

Section: Discussionmentioning

confidence: 99%

Analyzing the Effects of Gap Junction Blockade on Neural Synchrony via a Motoneuron Network Computational Model

Memelli

Horn

Wittie

et al. 2012

Computational Intelligence and Neuroscience

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In specific regions of the central nervous system (CNS), gap junctions have been shown to participate in neuronal synchrony. Amongst the CNS regions identified, some populations of brainstem motoneurons are known to be coupled by gap junctions. The application of various gap junction blockers to these motoneuron populations, however, has led to mixed results regarding their synchronous firing behavior, with some studies reporting a decrease in synchrony while others surprisingly find an increase in synchrony. To address this discrepancy, we employ a neuronal network model of Hodgkin-Huxley-style motoneurons connected by gap junctions. Using this model, we implement a series of simulations and rigorously analyze their outcome, including the calculation of a measure of neuronal synchrony. Our simulations demonstrate that under specific conditions, uncoupling of gap junctions is capable of producing either a decrease or an increase in neuronal synchrony. Subsequently, these simulations provide mechanistic insight into these different outcomes.

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Arrhythmia as a Result of Poor Intercellular Coupling in the Sinus Node: A Simulation Study

Cited by 14 publications

References 48 publications

Intracellular Ca dynamics in ventricular fibrillation

Intracellular Ca dynamics in ventricular fibrillation

Upregulation of Adenosine A1 Receptors Facilitates Sinoatrial Node Dysfunction in Chronic Canine Heart Failure by Exacerbating Nodal Conduction Abnormalities Revealed by Novel Dual-Sided Intramural Optical Mapping

Analyzing the Effects of Gap Junction Blockade on Neural Synchrony via a Motoneuron Network Computational Model

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