Biology subjects

Iavarone, E.

Publications and source records attributed to Iavarone, E..

4 recordsLinked to original sources

bioRxiv2025

Shaping a Collaborative, Sustainable, Accessible, and Reproducible Future for Computational Modeling

The o2S2PARC platform is an open-source, extensible, and scalable cloud-based platform developed in the context of the U.S. National Institutes of Health SPARC program to support collaborative, sustainable, FAIR (findable, accessible, interoperable, reusable) and reproducible computational modeling and analysis. This publication presents the main features of o2S2PARC, its underlying approaches and philosophy, innovative aspects of the developed technologies, while also drawing attention to its rapid adoption. The paper showcases a variety of applications and use cases enabled by the platform. These include hybrid electromagnetic-electrophysiology simulations of neural interfaces, personalized brain and spinal cord stimulation planning, in silico device safety assessments, the training and application of AI systems (e.g., for model-predictive control and medical image segmentation), hybridized surrogate modeling and multi-objective optimization in high-dimensional parameter spaces, sensitive and unbiased validation of measurement devices, and interactive data analysis as paper supplements.

neuroscience↗

bioRxiv2022

Reconstruction and simulation of thalamoreticular microcircuitry

Thalamoreticular circuitry is known to play a key role in attention, cognition and the generation of sleep spindles, and is implicated in numerous brain disorders, but the cellular and synaptic mechanisms remain intractable. Therefore, we developed the first detailed computational model of mouse thalamus and thalamic reticular nucleus microcircuitry that captures morphological and biophysical properties of [~]14,000 neurons connected via [~]6M synapses, and recreates biological synaptic and gap junction connectivity. Simulations recapitulate multiple independent network-level experimental findings across different brain states, providing a novel unifying cellular and synaptic account of spontaneous and evoked activity in both wakefulness and sleep. Furthermore, we found that: 1.) inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness, in addition to its role in spindle generation; 2.) thalamic interactions generate the characteristic waxing and waning of spindle oscillations; and 3.) changes in thalamic excitability (e.g. due to neuromodulation) control spindle frequency and occurrence. The model is openly available and provides a new tool to interpret spindle oscillations and test hypotheses of thalamoreticular circuit function and dysfunction across different network states in health and disease.

neuroscience↗

bioRxiv2019

Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.\n\nAuthor summaryThalamocortical neurons are one of the main components of the thalamocortical system, which are implicated in key functions including sensory transmission and the transition between brain states. These functions are reflected at the cellular level by the ability to generate action potentials in two distinct modes, called burst and tonic firing. Biophysically-detailed computational modeling of these cells can provide a tool to understand the role of these neurons within thalamocortical circuitry. We started by collecting single cell experimental data by applying standardized experimental procedures in brain slices of the rat. Prior work has demonstrated that biological constraints can be integrated using multi-objective optimization to build biologically realistic models of neuron. Here, we employ similar techniques as those previously employed, but extend them to capture the multiple firing modes of thalamic neurons. We compared the model results with additional experimental data test their generalization and quantitatively reject those that deviated significantly from the experimental variability. These models can be readily integrated in a data-driven pipeline to reconstruct and simulate circuit activity in the thalamocortical system.

neuroscience↗

bioRxiv2018

Aperiodic Rhythms of the Inferior Olive

Inferior olivary activity causes both short-term and long-term changes in cerebellar output underlying motor adaptation and motor learning, respectively. Many of its neurons engage in coherent subthreshold oscillations and are extensively coupled via gap junctions. Studies in reduced preparations suggest that these properties promote rhythmic, synchronized output. However, how these properties interact with synaptic inputs controlling inferior olivary output in intact, awake behaving animals is poorly understood. Here we combine electrophysiological recordings in awake mice with a novel and realistic tissue-scale computational model of the inferior olive to study the relative impact of intrinsic and extrinsic mechanisms governing its activity. Our data and model suggest that oscillations are present in the awake state, but that the period of subthreshold oscillations is variable, resulting in semi-periodic activity, which result in the high unpredictability of sensory-triggered complex spikes occurring in windows of opportunity lasting a few tens of milliseconds. To test the resonant properties of complex spike firing we used different temporal patterns of sensory stimulation in awake mice. Resonance was found to be limited to short intervals no more than a few hundred milliseconds, which could be explained by our network model to be a result of synaptic input to the inferior olive. The model also shows how gap junctional coupling stiffens the olivary network response to sensory modulation of complex spike rhythmicity. Interactions between intrinsic properties and extrinsic inputs can explain short-lasting semi- periodic variations of the spiking rhythms of olivary neurons even though their long-term average firing rate is stable, providing a conceptual framework for the creation of both the short-term and long-term changes in cerebellar output.\n\nAUTHOR SUMMARYActivity of the inferior olive, transmitted via climbing fibers to the cerebellum, regulates initiation and amplitude of movements, signals unexpected sensory feedback, and directs cerebellar learning. It is characterized by widespread subthreshold oscillations and synchronization promoted by strong electrotonic coupling. In brain slices, subthreshold oscillations set a temporal framework determining which inputs can be transmitted by inferior olivary neurons and which will not - dependent on the phase of the oscillation. In our study, we tested whether the subthreshold oscillations had any impact on temporal patterning of climbing fiber activity in intact, awake mice. We did so by recording neural activity of the postsynaptic Purkinje cells, in which complex spike firing faithfully represents climbing fiber activity. For short intervals (<300 ms), we found that many Purkinje cells indeed showed spontaneously rhythmic complex spike activity. However, our experiments designed to evoke resonant responses clearly indicated that complex spikes are not predicated on stimulus history. Our realistic network model of the inferior olive explains the experimental findings via continuous phase modulations of the subthreshold oscillations. We conclude that inferior olivary activity is emerging from an aperiodic rhythm, stabilized by electrotonic coupling, and dependent on the behavioral context transmitted via synaptic input.

neuroscience↗