Elisabetta Iavarone

Related publications (5)

Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep

Marwan Muhammad Ahmed Abdellah, Oren Amsalem, Marine Laure Bertschy, Grigori Chevtchenko, Benoît Jean-Albert Coste, Jean-Denis Georges Emile Courcol, András Ecker, Cyrille Pierre Henri Favreau, Adrien Christian Fleury, Michael Emiel Gevaert, Jayakrishnan Harikrishnavilas Raveendran Nair, Joni Henrikki Herttuainen, Sean Lewis Hill, Elisabetta Iavarone, Genrich Ivaska, Anna-Kristin Kaufmann, Samuel Claude Kerrien, James Gonzalo King, Pramod Shivaji Kumbhar, Polina Litvak, Patrycja Lurie, Ioannis Magkanaris, Henry Markram, Vignayanandam Ravindernath Muddapu, Rodrigo de Campos Perin, Fabien Petitjean, Michael Reimann, Nadir Roman Guerrero, Ying Shi, Liviu Soltuzu, Mohameth François Sy, Mustafa Anil Tuncel, Alexander Désiré Ulbrich, Werner Alfons Hilda Van Geit, Matthias Wolf

2023

The thalamus, once believed to be a simple relay station between the body periphery and the neocortex, has started to be recognized as a key player in higher-order functions, such as attention. It participates in the transition between brain states, such as sleep and wakefulness, and thalamocortical oscillations. Many of these functions involve the reticular nucleus (Rt), an inhibitory brain structure that surrounds the thalamus and where thalamocortical and corticothalamic axons establish synaptic contacts. Recent technical advances have made possible the study of thalamic activity in vivo and its contributions to cortical processing and behavior. However, a unified understanding of how network dynamics in the thalamus and the thalamocortical system is shaped by its neurons and synapses is still lacking. To address these challenges, we developed biophysically-detailed computational models of thalamic and reticular neurons. We then incorporated them in a large-scale model of thalamo-reticular microcircuitry, whose connectivity was directly constrained by three-dimensional reconstructions of neuronal morphologies. We included intrathalamic connections through chemical synapses, gap junctions and synapses from the sensory periphery and the neocortex. A large part of the data used to build and validate the model was extracted from the literature and leveraged a framework for its systematic and collaborative curation. This framework allowed us to keep track of parameter values used in the model, along with metadata describing the species, brain regions and experimental conditions. As a first validation of the model at the network level, we studied the generation of spindle-like oscillations. We found that external inputs are necessary to initiate this rhythm, while its termination can be sustained by the synaptic interplay alone, with a key role of mutual inhibition between reticular neurons. We found that that waxing-and-waning oscillations have a clear spatial component, that reflected the connectivity through chemical synapses as well as gap junctions. Finally, we investigated how differential depolarization in reticular and thalamocortical neurons influence the properties of spindle-like oscillations and predicted conditions where they are less easy to evoke. Taken together this thesis demonstrates how a bottom-up modelling approach can be successfully applied to reconstruct and simulate thalamic microcircuitry, which shows emergent network behavior compatible with experimental findings, and paves the way for understanding the role of the thalamus in thalamocortical functions.

EPFL2020

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

Elisabetta Iavarone

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments.

Springer2019

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

Sean Lewis Hill, Elisabetta Iavarone, Henry Markram, Christian Andreas Rössert, Ying Shi, Werner Alfons Hilda Van Geit, Jane Yi, Bas-Jan Zandt

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.

2019

A Framework for Collaborative Curation of Neuroscientific Literature

Sean Lewis Hill, Elisabetta Iavarone

Large models of complex neuronal circuits require specifying numerous parameters, with values that often need to be extracted from the literature, a tedious and error-prone process. To help establishing shareable curated corpora of annotations, we have developed a literature curation framework comprising an annotation format, a Python API (NeuroAnnotation Toolbox; NAT), and a user-friendly graphical interface (NeuroCurator). This framework allows the systematic annotation of relevant statements and model parameters. The context of the annotated content is made explicit in a standard way by associating it with ontological terms (e.g., species, cell types, brain regions). The exact position of the annotated content within a document is specified by the starting character of the annotated text, or the number of the figure, the equation, or the table, depending on the context. Alternatively, the provenance of parameters can also be specified by bounding boxes. Parameter types are linked to curated experimental values so that they can be systematically integrated into models. We demonstrate the use of this approach by releasing a corpus describing different modeling parameters associated with thalamo-cortical circuitry. The proposed framework supports a rigorous management of large sets of parameters, solving common difficulties in their traceability. Further, it allows easier classification of literature information and more efficient and systematic integration of such information into models and analyses.

Frontiers Research Foundation2017

EPFL2020

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

Elisabetta Iavarone

Springer2019

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

Sean Lewis Hill, Elisabetta Iavarone, Henry Markram, Christian Andreas Rössert, Ying Shi, Werner Alfons Hilda Van Geit, Jane Yi, Bas-Jan Zandt

2019