Sean Lewis Hill | EPFL Graph Search

Sean Hill is co-Director of Blue Brain, a Swiss national brain initiative, where he leads the Neuroinformatics division, based at the Campus Biotech in Geneva, Switzerland. He also directs the Laboratory for the Neural Basis of Brain States at the École Polytechnique Fédérale de Lausanne (EPFL). Dr. Hill served as the Executive Director (2011-2013) and Scientific Director (2014-2016) of the International Neuroinformatics Coordinating Facility (INCF) at the Karolinska Institutet in Stockholm, Sweden. Dr. Hill has extensive experience in building and simulating large-scale models of brain circuitry and has also supervised and led research efforts exploring the principles underlying the structure and dynamics of neocortical and thalamocortical microcircuitry. He currently serves in management and advisory roles on several large-scale clinical informatics initiatives around the world. After completing his Ph.D. in computational neuroscience at the Université de Lausanne, Switzerland, Dr. Hill held postdoctoral positions at The Neurosciences Institute in La Jolla, California and the University of Wisconsin, Madison, then joined the IBM T.J. Watson Research Center where he served as the Project Manager for Computational Neuroscience at Blue Brain until his appointment at the EPFL.

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