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Neuroinformatics Assembly 2024 - Sessions


Session details

Session 1

FAIR neuroscience

Keynote: Timo Dicksheid, PhD, Jülich Research Center
International Brain Data Governance Frameworks: facilitating global research
Speaker: Kim Ray, University of Texas Austin
Facilitating standardization and sharing of neurophysiology data
Speaker: Ben Dichter, CatalystNeuro
Enhancing the findability, accessibility, interoperability, and reusability of neuroscience data using the openMINDS metadata framework
Speaker: Andrew P. Davison, Paris-Saclay Institute of Neuroscience, CNRS, Université Paris-Saclay
Establishing an early indicator for data sharing and reuse
Speaker: Anita Bandrowski, SciCrunch Inc.
NIDM-Experiment: Annotation of Neuroscientific Data and the FAIR Principles
Speaker: Karl Helmer, Massachusetts General Hospital.
Session 2

Bridging the gap between neuroimaging and the broader data science ecosystem

Keynote: Hanchuan Peng, PhD, SEU-ALLEN Joint Center
Facilitating standardization and sharing of neurophysiology data
Speaker: Gregory Kiar, Child Mind Institute
GPU-accelerated connectome discovery at scale
Speaker: Devarajan Sridharan, Indian Institute of Science, Bangalore
Neurodesk: Enabling Reproducible and Collaborative Neuroscience through Software Containers
Speaker: Thuy Dao, The University of Queensland
Community-engaged benchmarking of diffusion MRI processing workflows using a concordance analysis
Speaker: Jelle Veraart, NYU Grossman School of Medicine
Talk title coming soon
Speaker: Sarah Rachel Heilbronner, Baylor College of Medicine
Talk title coming soon
Speaker: Anastasia Yendiki, Harvard Medical School
Talk title coming soon
Speaker: Nuno da Costa, Allan Institute for Brain Science
Session 3

Applications of AI to neuroscience research

Keynote: Danilo Bzdok, MD, PhD, Mila - Quebec Artificial Intelligence Institute
Feasibility of a Natural Language Query Interface for the SPARC Connectivity Knowledge Base
Speaker: Fahim Imam, University of California, San Diego
Explainable AI as a Tool for Investigating the Computational Bases of Cognitive Processing
Speaker: Evie Malaia, University of Alabama
Brain-computer interfaces: How Machine Learning Can Support Subject Training
Speaker: José del R. Millán, University of Texas at Austin
Brain-computer interfaces: Applications of Differentiable Programming and Generative AI in MRI
Speaker: Christophe Lenglet, University of Minnesota Medical School
Talk title coming soon
Speaker: Sanmi Koyejo, Stanford University
Session 4 

Closing the discovery loop and digital twins

Keynote: Viktor Jirsa, PhD, Institut de Neurosciences des Systemès
Merging predictive and generative models for the creation of digital twins
Speaker: Randy McIntosh, Simon Fraser University
Acute Stroke Imaging, Pushing the Boundaries of Existing Modalities with Machine Learning
Speaker: Luca Giancardo, University of Texas Healh Science Center at Houston
Special guest session


Keynote: Elisabeth Bik, PhD, Microbiome Digest

Training sessions

Training details

Training 1

Driving collaboration in neurophysiology with NWB and DANDI

Lead: Ben Dichter, CatalystNeuro
    Learn how to:
  • Convert neurophysiology data to NWB
  • Upload and publish data on DANDI
  • Search the archive for relevant datasets
  • Use integrated analysis and visualization tools
  • Share analysis projects with others
Training 2

Introduction to reproducible neuroimaging data processing and analysis

Lead: Franco Pestilli, University of Texas Austin
Embark on a comprehensive journey into the realm of neuroimaging data processing and analysis with our innovative educational course tailored for graduate students, postdocs and early stage investigators. Leveraging the cutting-edge capabilities of the platform (Hayashi, Caron et al., Nature Methods 2024). This course offers a unique blend of theory lectures and hands-on practical tutorials, providing students with a deep understanding of MRI data processing techniques (functional, diffusion and anatomical MRI). Through engaging lectures, participants will delve into the theoretical foundations underpinning neuroimaging analysis, while practical tutorials will empower them to apply these concepts in real-world scenarios. By harnessing the power of, students will gain invaluable experience navigating through neuroimaging datasets, performing analyses, and interpreting results, preparing them to embark on impactful research endeavors in the field of neuroscience.
Training 3

DataJoint pipelines for your neuroscience experiments

Lead: Dimitri Yatsenko, DataJoint