Bushy neurons of the cochlear nucleus encode temporal fine structure and modulation of sound with high fidelity. However, the synaptic maps and electrotonic structures that underlie these properties are not specified in meaningful explanatory detail. We employed modern volume electron microscopy techniques to provide exact data on the numbers of synaptic inputs and their weights determined by the number of contained active zones, and the surface areas of all postsynaptic cellular compartments. Leveraging these high-resolution images, we discovered cabling of dendrite branches and new structures within dendrites, and identified non-innervated dendrites. We extend current nanoscale connectomic studies with methods to export cellular reconstructions into morphologically-constrained, biophysically-based predictive computational models. We reveal both coincidence detection and mixed supra/subthreshold modes of input convergence across the bushy cell population and show subthreshold inputs contribute to enhanced temporal encoding even in the presence of suprathreshold inputs. We demonstrate the variation of dendritic load and axon parameters and their importance in controlling excitability as potential homeostatic mechanisms, thereby defining heterogeneity in stimulus-evoked responses across the BC population.

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Globular bushy cells (GBCs) of the cochlear nucleus play central roles in the temporal processing of sound. Despite investigation over many decades, fundamental questions remain about their dendrite structure, afferent innervation, and integration of synaptic inputs. Here, we use volume electron microscopy (EM) of the mouse cochlear nucleus to construct synaptic maps that precisely specify convergence ratios and synaptic weights for auditory- nerve innervation and accurate surface areas of all postsynaptic compartments. Detailed biophysically-based compartmental models can help develop hypotheses regarding how GBCs integrate inputs to yield their recorded responses to sound. We established a pipeline to export a precise reconstruction of auditory nerve axons and their endbulb terminals together with high-resolution dendrite, soma, and axon reconstructions into biophysically-detailed compartmental models that could be activated by a standard cochlear transduction model. With these constraints, the models predict auditory nerve input profiles whereby all endbulbs onto a GBC are subthreshold (coincidence detection mode), or one or two inputs are suprathreshold (mixed mode). The models also predict the relative importance of dendrite geometry, soma size, and axon initial segment length in setting action potential threshold and generating heterogeneity in sound-evoked responses, and thereby propose mechanisms by which GBCs may homeostatically adjust their excitability. Volume EM also reveals new dendritic structures and dendrites that lack innervation. This framework defines a pathway from subcellular morphology to synaptic connectivity, and facilitates investigation into the roles of specific cellular features in sound encoding. We also clarify the need for new experimental measurements to provide missing cellular parameters, and predict responses to sound for further in vivo studies, thereby serving as a template for investigation of other neuron classes.

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Introduction: Although rotor modulation targeting atrial fibrillation (AF) drivers or substrates has been proposed as one of the effective ablation strategies for non-paroxysmal AF (Non-PAF), previous meta-analyses demonstrated that ablation added to pulmonary vein isolation (PVI) did not result in the expected outcome. This is because the optimal method of detection of rotors and ablation strategy remain unclear. Recently, rotor detection using LGE-MRI-based computer simulations has been shown to be effective for Non-PAF ablation in clinical practice. Our study aimed to establish the minimal ablation strategy that, while correctly finding the rotors, also avoids iatrogenic atrial tachyarrhythmias due to excessive ablation. Hypothesis: By prioritizing and classifying detected rotors, reentrant drivers (RDs) of AF rather than passive rotors (PRs) could be identified, thereby offering an optimal ablation strategy. Methods: Personalized computational modeling of AF ablation was performed in 10 Non-PAF patient models based on fibrosis data from LGE-MRI. In each bi-atrial model, all rotors induced outside of PVI were investigated, and a number of ablation strategies were examined sequentially to classify rotors and achieve minimal ablation (figure). A rotor that terminated following ablation of another rotor was defined as PR. A rotor that persisted and needed to be ablated to achieve non-inducibility of the substrate was defined as RD. Results: Seven patients had rotors outside of PVI, with 6 having both PRs and RDs. Overall, 35 rotors were induced, 13 in left atrium and 22 in the right; 17 were RDs and 18 PRs. In addition, the density of fibrosis in the sites of RDs was significantly higher than in those of PRs (p=0.031). Conclusion: The sequential computer simulation strategy to predict ablation targets using a personalized AF model is promising in detecting different types of rotors and establishing the optimal minimum-lesion ablation strategy.