Sean Lewis Hill

Related publications (46)

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Pascal Fua, Sean Lewis Hill, Xiang Li, Amos Sironi, Yuhang Wang, Jian Yang, Jian Zhou

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings. This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.

NATURE PORTFOLIO2023

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Michaël Fernand Paul Dupont, Pierre-Alexandre Fonta, Alejandra Garcia Rojas Martinez, Henry Genet, Sean Lewis Hill, Silvia Rosario Jimenez Tejeda, Anna-Kristin Kaufmann, Samuel Claude Kerrien, Ian Lavriushev, Carolina Johanna Elisabeth Lindqvist, Huanxiang Lu, Jonathan Raël Lurie, Julien Antonin Machon, Henry Markram, Didac Montero Mendez, Dhanesh Neela Mana, Kenneth William Pirman, Bogdan Roman, Natalia Stafeeva, Mohameth François Sy, Alexander Désiré Ulbrich, Wojciech Adam Wajerowicz

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simulating the whole rodent brain in the Swiss EPFL Blue Brain Project (BBP) required a solution to managing large-scale highly heterogeneous data, and tracking their provenance to ensure quality, reproducibility and attribution throughout these iterative cycles. Here, we describe Blue Brain Nexus (BBN), an ecosystem of open source, domain agnostic, scalable, extensible data and knowledge graph management systems built by BBP to address these challenges. BBN builds on open standards and interoperable semantic web technologies to enable the creation and management of secure RDF-based knowledge graphs validated by W3C SHACL. BBN supports a spectrum of (meta)data modeling and representation formats including JSON and JSON-LD as well as more formally specified SHACL-based schemas enabling domain model-driven runtime API. With its streaming event-based architecture, BBN supports asynchronous building and maintenance of multiple extensible indices to ensure high performance search capabilities and enable analytics. We present four use cases and applications of BBN to large-scale data integration and dissemination challenges in computational modeling, neuroscience, psychiatry and open linked data.

2023

Technology-Enabled Collaborative Care for Concurrent Diabetes and Distress Management During the COVID-19 Pandemic: Protocol for a Mixed Methods Feasibility Study

Sean Lewis Hill

Background: The COVID-19 pandemic disrupted the delivery of diabetes care and worsened mental health among many patients with type 2 diabetes (T2D). This disruption puts patients with T2D at risk for poor diabetes outcomes, especially those who experience social disadvantage due to socioeconomic class, rurality, or ethnicity. The appropriate use of communication technology could reduce these gaps in diabetes care created by the pandemic and also provide support for psychological distress. Objective: The purpose of this study is to test the feasibility of an innovative co-designed Technology-Enabled Collaborative Care (TECC) model for diabetes management and mental health support among adults with T2D. Methods: We will recruit 30 adults with T2D residing in Ontario, Canada, to participate in our sequential explanatory mixed methods study. They will participate in 8 weekly web-based health coaching sessions with a registered nurse, who is a certified diabetes educator, who will be supported by a digital care team (ie, a peer mentor, an addictions specialist, a dietitian, a psychiatrist, and a psychotherapist). Assessments will be completed at baseline, 4 weeks, and 8 weeks, with a 12-week follow-up. Our primary outcome is the feasibility and acceptability of the intervention, as evident by the participant recruitment and retention rates. Key secondary outcomes include assessment completion and delivery of the intervention. Exploratory outcomes consist of changes in mental health, substance use, and physical health behaviors. Stakeholder experience and satisfaction will be explored through a qualitative descriptive study using one-on-one interviews. Results: This paper describes the protocol of the study. The recruitment commenced in June 2021. This study was registered on October 29, 2020, on ClinicalTrials.gov (Registry ID: NCT04607915). As of June 2022, all participants have been recruited. It is anticipated that data analysis will be complete by the end of 2022, with study findings available by the end of 2023. Conclusions: The development of an innovative, technology-enabled model will provide necessary support for individuals living with T2D and mental health challenges. This TECC program will determine the feasibility of TECC for patients with T2D and mental health issues. Trial Registration: ClinicalTrials.gov NCT04607915; https://clinicaltrials.gov/ct2/show/NCT04607915 International Registered Report Identifier (IRRID): DERR1-10.2196/39724

JMIR PUBLICATIONS, INC2023

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 challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological, and synaptic properties. Competency queries to this knowledge base demonstrate that the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature.

HUMANA PRESS INC2022

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The binding of GABA (gamma-aminobutyric acid) to extrasynaptic GABA(A) receptors generates tonic inhibition that acts as a powerful modulator of cortical network activity. Despite GABA being present throughout the extracellular space of the brain, previous work has shown that GABA may differentially modulate the excitability of neuron subtypes according to variation in chloride gradient. Here, using biophysically detailed neuron models, we predict that tonic inhibition can differentially modulate the excitability of neuron subtypes according to variation in electrophysiological properties. Surprisingly, tonic inhibition increased the responsiveness (or gain) in models with features typical for somatostatin interneurons but decreased gain in models with features typical for parvalbumin interneurons. Patch-clamp recordings from cortical interneurons supported these predictions, and further in silico analysis was then performed to seek a putative mechanism underlying gain modulation. We found that gain modulation in models was dependent upon the magnitude of tonic current generated at depolarized membrane potential-a property associated with outward rectifying GABA(A) receptors. Furthermore, tonic inhibition produced two biophysical changes in models of relevance to neuronal excitability: 1) enhanced action potential repolarization via increased current flow into the dendritic compartment, and 2) reduced activation of voltage-dependent potassium channels. Finally, we show theoretically that reduced potassium channel activation selectively increases gain in models possessing action potential dynamics typical for somatostatin interneurons. Potassium channels in parvalbumin-type models deactivate rapidly and are unavailable for further modulation. These findings show that GABA can differentially modulate interneuron excitability and suggest a mechanism through which this occurs in silico via differences of intrinsic electrophysiological properties.

NATL ACAD SCIENCES2020

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

Reconstruction and simulation of neocortical microcircuitry

Sean Lewis Hill, Henry Markram, Eilif Benjamin Muller

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reconstructing and simulating neocortical microcircuitry. In one aspect, a method includes providing a model of neural tissue, the model including different types of neural cells and dynamic synaptic interconnections between the neural cells, changing a parameter in the model; and identifying a change in a computational state of the model of the neural tissue responsive to the change in the parameter. The change in the parameter can, e.g., change behavior of neural cells of at least one type, change interconnectivity between neural cells, or target a location within a volume in the model that interacts with multiple types of neural cells.

2018

Towards a supervised classification of neocortical interneuron morphologies

Ruth Benavides Piccione, Sean Lewis Hill, Bojan Mihaljevic

BackgroundThe challenge of classifying cortical interneurons is yet to be solved. Data-driven classification into established morphological types may provide insight and practical value.ResultsWe trained models using 217 high-quality morphologies of rat somatosensory neocortex interneurons reconstructed by a single laboratory and pre-classified into eight types. We quantified 103 axonal and dendritic morphometrics, including novel ones that capture features such as arbor orientation, extent in layer one, and dendritic polarity. We trained a one-versus-rest classifier for each type, combining well-known supervised classification algorithms with feature selection and over- and under-sampling. We accurately classified the nest basket, Martinotti, and basket cell types with the Martinotti model outperforming 39 out of 42 leading neuroscientists. We had moderate accuracy for the double bouquet, small and large basket types, and limited accuracy for the chandelier and bitufted types. We characterized the types with interpretable models or with up to ten morphometrics.ConclusionExcept for large basket, 50 high-quality reconstructions sufficed to learn an accurate model of a type. Improving these models may require quantifying complex arborization patterns and finding correlates of bouton-related features. Our study brings attention to practical aspects important for neuron classification and is readily reproducible, with all code and data available online.

BMC2018

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