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Icon for: Zoran Popović


University of Washington, Center for Game Science


NSF Awards: 1546510, 1551063

2017 (see original presentation & discussion)

Grades 9-12, Undergraduate, Graduate, Adult learners, Informal / multi-age

Understanding how our brain works - and how we use this to tackle a myriad of brain diseases – is one of the biggest challenges facing humanity.  Mozak is a scientific discovery game about neuroscience and allows players to explore models of brain cells. We help scientists learn more about the brain through reconstructing neurons by bringing together groups of differently-talented people to solve this enormous problem together. Mozak’s online interface has already enabled teams of middle schoolers to reconstruct neurons rapidly - with no prior expertise – and a new generation of reconstructors are now emerging from the beta phase of the project. This video explores how Mozak came together, what we’re already achieving, and how we plan to help our gaming community become part of a new highly trained global neuroscience workforce capable of taking on more complex challenges (and maybe even becoming scientists themselves!).

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Discussion from the 2017 STEM for All Video Showcase (6 posts)
  • Icon for: Tami LaFleur

    Tami LaFleur

    K-12 STEM Coordinator
    May 15, 2017 | 09:16 a.m.

    Very intriguing work! Are you saying that through gaming, students can re-construct neurons on their brains to improve function? When you write that the students had "no prior expertise," did you mean expertise of gaming or knowledge of how the brain works? I look forward to hearing more about this project.

    Discussion is closed. Upvoting is no longer available

    Michael Kolodziej
  • Icon for: Zoran Popović

    Zoran Popović

    Lead Presenter
    May 17, 2017 | 07:33 p.m.

    How it works is that we receive what is called a "dataset" from the Allen Institute for Brain Science. It is akin to an MRI of a single neuron. Players then trace along all parts of the neuron (very carefully following the lines). And what we end up with is a 3D reconstruction of that neuron that we pass along to neuroscientists to study. The human visual system is far better at evaluating these types of images than a computer and it is too time-consuming for neuroscientists to make much headway on alone. And so enters Mozak! We are working hard to get the same level of accuracy as expert reconstructions but much, much quicker. Novice citizen neuroscientists are already out-performing computers. With minimal oversight, citizens generate reconstructions that are 70-90% complete. The most effective, computer-generated reconstructions are frequently only 10-20% complete. The ultimate goal is to help better understand the brain and the diseases that do it harm. It's very exciting stuff. Feel free to check out the game at www.mozak.science if you want to see it in action. We also keep in touch with our community through social media outlets like Facebook and Twitter.

  • Icon for: Dale McCreedy

    Dale McCreedy

    Vice President of Audience & Community Engagement
    May 16, 2017 | 10:24 a.m.

    Fascinating - love the focus on brain development and inclusion of citizen scientists. How do members become part of the Mozak community?  Like Tami, I am wanting more detail about what it looks like for citizens are actually doing?  What does it mean to 'trace neurons'?

  • Icon for: Zoran Popović

    Zoran Popović

    Lead Presenter
    May 17, 2017 | 07:51 p.m.

    Those wanting to play and contribute can head to www.mozak.science and sign up for an account. We are also active on Facebook and Twitter. Tracing a neuron basically looks like very fine, careful tracing the lines of images of a neuron (these are called datasets - they are akin to an MRI of a neuron). The human visual system is uniquely equipped to take on this task, more so than a computerized system. Players' traces are then checked against other players' traces, in a process called "consensus". Running everyone's trace through this consensus process gives us 3D reconstructions of these neurons which we forward on to neuroscientists to classify and study. Mozak has allowed scientists at Allen Institute for Brain Science to switch away from their state-of-the-art tools to novice-oriented tools and observe a significant speedup in their work. With the switch to Mozak, there was a 3.6 fold increase in the number of full neuron reconstructions completed over the course of one year. It's all very exciting and we are thrilled with the contributions of our community!

  • Icon for: Michael Kolodziej

    Michael Kolodziej

    Associate Vice President
    May 17, 2017 | 03:26 p.m.

    I really like the idea of using technology to bridge the gap between expert practitioners and citizen scientists, and this project seems like it does that brilliantly. In addition to Tami and Dale's questions above, I wonder if you can speak to how the connection was made between the need for nerve reconstruction on a large scale, and the opportunity for citizen scientists to participate.  Are there any learnings that can be highlighted in terms of how you were able to build and deliver the platform that may inform other efforts?

    Thank you in advance for your thoughts.

  • Icon for: Zoran Popović

    Zoran Popović

    Lead Presenter
    May 22, 2017 | 07:58 p.m.

    The project emerged our of specific need by neuroscientists to reconstruct neuron structures, and not having enough human expertise.  The computational methods also didn't help because on these datasets they can get to only 20% of completeness.  Our lab has worked on Foldit for last 10 years, and it was clear to us that we can create an even better rapid expertise development environment in neuroscience by leveraging what we have learned about getting people engaged in science, acquiring skills quickly.  The biggest important novelty in my mind, is the near-real-time interaction between scientists and citizens that is contextualized within the actual neuron, so that know-how can be transferred rapidly while increasing engagement.   The second key insight relates to use of continuously computed consensus to promote both creative exploration, and agreement on correct reconstructions by people.  This enabled the community to advance in productive ways that provide both creative exploration and robust verification, even though the problem does not have a well-defined metric or score for a really good reconstruction.  This approach can be applied to many other scientific endeavors where measures of success are unknown.

  • Further posting is closed as the event has ended.