A Network Map Arranging Itself

A map created using Quid showing news articles about neuroscience discoveries made possible with DTI (Diffusion Tensor Imaging). DTI reveals tracts of white matter connectivity in the brain, allowing us to see which regions of the brain are talking to one another.

An Ecosystem of DTI Discoveries

DTI Neurotech network graph

About the map: Each of those colored dots is one news story. Dots are called nodes. They are connected by lines called edges. Using natural language processing, algorithms can read the text content of articles and assess content. What is the article about? What are the key concepts? This information creates a similarity matrix of sorts, describing nodes by attributes. These attributes define the physical layout of the network. Articles most closely linked are grouped in clusters called communities.

This map shows an initial set of 550 nodes arranged into network view. Each node repels other nodes, making the communities with fewer connections move farther apart and those with more connections cluster together. It’s all generated in Quid.

A network layout is just the first step. For it to be useful, I need to understand it.

Next comes my favorite: human exploration.

It takes work to make sense of a complex ecosystem of information. Here’s my technique to tackle a network: Make a first pass, exploring each cluster. As I slowly begin to familiarize myself with the graph, I give each community a name.The cluster names are added manually, so it’s helpful to explore the largest nodes and nodes that stand out. Along the way, I save articles of interest.

DTI neurotech quid map with community labels white

Once each community is named, it’s easier to dive into details that make a big picture – the individual stories of scientific discoveries. I particularly love looking at stories that bridge two or more distinct communities.

Now that I’ve collected links, it’s time to embark upon digesting 20+ articles and abstracts spanning things like white matter connectivity associated with self esteem, the neuroscience of risk-taking, and even the links between physical fitness and brain health. Stay tuned for my completed thoughts next month in Scientific American and hit me up on Twitter if this post gave you any ideas!

network map sketch, notes, network, complex network, sketch

BRAIN Initiative: Thoughts on Interim Report to the NIH

neurons reconstructed in eyewire, connectome

We stand on the verge of a great journey into the unknown—the interior terrain of thinking, feeling, perceiving, learning, deciding, and acting to achieve our goals—that lives within the human brain. These capacities are the essence of our minds and the aspects of being human that matter most to us. Remarkably, these powerful yet exquisitely nuanced capacities emerge from electrical and chemical interactions among roughly 100 billion nerve cells and glial cells that compose our brains. All human brains share basic anatomical circuits and synaptic interactions, but the precise pattern of connections and interactions are highly variable from person to person—and therein lies the source of the remarkable variation we see in human behavior, from the breathtaking dance of a ballerina, to the elegant craftsmanship of a master carpenter, to the shrewd judgment of an expert trader. Our brains make us who we are, enabling us to perceive beauty, teach our children, remember loved ones, react against injustice, learn from history, and imagine a different future.

Preamble, The Goals of the BRAIN Initiative

Today is my one year anniversary at MIT. I’ve grown as a person and learned more than I ever imagined. To celebrate, I read a 50+ page NIH report, which was surprisingly awe inspiring. Here are my thoughts.

On Sept 16, 2013, an advisory committee of prominent neuroscientists presented a report to the NIH Director outlining the goals and objectives of the BRAIN Initiative for FY 2014.

Before we proceed, Iet’s back up. My greatest passion in life is understanding consciousness; understanding how the brain yields a thinking, feeling human being. That’s why I’m at Seung Lab working on EyeWire, attempting to catalyze exponential progress advancing neuroscience from the unknown into the understood. How exciting, how chill-inducing to read that the greatest minds on minds of our generation are teaming up and calling for unprecedented action in this direction, progress backed with investment and goals for disruptive, interdisciplinary collaboration that “reconceives what it means to be an experimental neuroscientist.” (13)

The report outlines several themes and high-priority research areas for 2014:

  1. Generate a Census of Cell Types
  2. Create Structural Maps of the Brain
  3. Develop New Large-Scale Network Recording Capabilities
  4. Develop A Suite of Tools for Circuit Manipulation
  5. Link Neuronal Activity to Behavior
  6. Integrate Theory, Modeling, Statistics, and Computation with Experimentation
  7. Delineate Mechanisms Underlying Human Imaging Technologies
  8. Create Mechanisms to Enable Collection of Human Data
  9. Disseminate Knowledge and Training

I am particularly excited about, well, all of these but for the interest of time I’ll hone it down. First, let’s reflect on the fact that we don’t even know how many types of cells there are in our own heads..and how quickly that’s changing. A catalog of cell types would provide a framework for existing research and a foundation for future experiments. Like everything in this report, it’s extremely exciting.

Second. Create a structural map of the brain. The report calls for a movement “towards [mapping] a full connectome.” Oh yes. There are many layers of functional circuits in the brain, all of which are important to our broader understanding. Special emphasis is given to integrating scales in both time and space and creating platforms that “enable understanding of the relation between neural structure and function.” The report calls for “faster, less expensive, and scalable approaches” that will reveal how how neural dynamics relate to complex behavior. Crowd-sourcing is specifically mentioned in this section, which needless to say amps me up.

neuron branches, eyewire,

I’ll skip now to numbers 5 and 6, which read as a call for revolutionary cross-boundary collaboration among researchers to create integrative tools for creating enriched, multidimensional datasets that, for example, might integrate molecular, functional and connectomic information. The report indicates a preferences for involvement from fields outside neuroscience such as computer science, statistics, engineering, physics and theory (and even calls for new theoretical tools and techniques — a “brain based theory of higher functions is notably lacking.” hello, opportunity).

This is not the time to play is safe.

I love this. Neuroscience is due a bit of disruption; we need to bring in minds from different fields — fields that the authors of this report may not have even considered, such as design. I think crucial components will be identifying and communicating neuroscience’s biggest accomplishments, current state of knowledge and present/future hurdles to communities of talented individuals and organizations, many of which may be outside academia, who will develop and test innovative solutions to them. We may use tools like Kaggle or host Hackathons, challenging a burgeoning global developer community to create software that will help solve neuroscience’s biggest difficulties. I’ve come to realize that while techniques do cost a considerable amount of time, it seems that software and big data analysis are what’s really needed to increase technolgical and analytical throughput by 100-1000x, as the report calls for. One infamous case of analytic delay is that of Earl Miller’s Lab at MIT. The team spent 2 weeks collecting functional activity data and 2 years analyzing it. Damn. We need tools, better AI and brave collaborations on all neruoscientific fronts. This is one area I’m particularly interested in catalyzing.

Another interesting component of this research is an open call to create a neuroscience data reservoir where researchers can store and one day maybe even crowd-source analysis of their research findings, specifically image data. Seung Lab (my lab) has an interest in this, so stay tuned.

synapse in eyewire

A final point of interest is #9, the dissemination of knowledge and training. In the context of citizen science, which will likely play an increasingly important role in neuroscientific progress, we need to share best practices and methods through which labs can involve the general public in the scientific method. We are actively working on such toolkits and events. Ping me if you are interested in collaborating on this or any other area.

Finally, going back to the big picture (my favorite), I’d like to point out that the report specifically calls for research “composed with a specific eye toward eventual impact for humans” and that “encourages changes in the culture of neuroscience.”

The challenge is to map the circuits of the brain, measure the fluctuating patterns of electrical and chemical activity flowing within those circuits, and understand how their interplay creates our unique cognitive and behavioral capabilities.

Our ultimate goal is to understand our own brains.. to understand the circuits and patterns of neural activity that give rise to mental experience and behavior.

It’s a wonderful time to be alive. I’m honored to have the opportunity to play a leading role in the future of neuroscience through EyeWire and specifically Seung Lab at MIT.

for science eyewire black on yellow, for science, eyewire, brain, design

What does the world look like through the eyes of a neuroscientist?

connectome project

How does the world look through the eyes of neuroscience?

Paul King of the Redwood Center for Theoretical Neuroscience at UC Berkeley sums up his perspective in 7 points on Quora:

1. Body image is dynamic and flexible.
2. Perceptual reality is entirely generated by our brain.
3. We see the world in narrow disjoint fragments.
4. Our behavior is mostly automatic.
5. Our brain can fool itself in really strange ways.
6. Neurons are really slow.
7. Consciousness can be subdivided.

Read the full answer on the EyeWire Blog.

Image courtesy of the Laboratory of NeuroImaging at UCLA and Martinos Center for Biomedical Imaging at MGH, Consortium of the Human Connectome Project.

Live Broadcast from the Shoulders of Giants or How (Funny) Videos Augment Science Communication

In the past week, over 10,000 people have joined EyeWire, a citizen science neuron mapping game from Seung Lab at MIT.

I’m about 8 weeks new in this wonderful lab and have started mixing things up a bit. What do typical researchers do on a daily basis? Who knows! They’re spectacular and secretive creatures. Seung Lab is not so secretive. Dev ninjas have even started doing spontaneous video sessions (who knew you could catch a ninja on video?!) and our whole team hosted a G+ hangout during a Christmas party, which might have included copious amounts of alcohol, dancing and wouldn’t have been complete without popping champagne. Our cameras are locked and loaded — charged and mic’d up. Don’t you love it when scientists have personality (yea that’s a GIF of Sebastian dancing created by EyeWirer Dylan Holtz), a sense of humor and are willing to share it with the world? I sure as hell do. I think it makes science more attractive and accessible, which is, in the words of brewery Sam Adams, always a good decision.

As you can see, we take science — but not ourselves —  seriously here.

i dont always pretend to be a scientist but when I do it's on EyeWire.org

That was created by EyeWirer Adam Brabant.

Seriously, though, Why EyeWire? You could read this post….or watch the video below.

We interviewed Harvard’s Joshua Sanes (a legend among neuroscientists.. though admittedly I did not know this chap until I learned he discovered a new cell, which made me wonder: if the EyeWire community discovers new cells, do they get to name them?  If I have anything to say about it, new cells will not be called “Junctional Adhesion Molecule B” Cells. No, no. I’d opt for the “epic” neuron so that people come to know us as the Discoverers of Epic).

A little EyeWire Tutorial:

A fun video we made when I Fucking Love Science + Reddit crashed EyeWire on Tuesday:

More videos are coming, including action-packed theatrical trailers, ultramicrotome drama, and dancing (lots of dancing) from Sebastian. Oh yea, and educational productions. After all, we do want to create a smarter world. I leave you with this: Sebastian goes Gangnam style. Created by Seung Lab’s great Spaniard postdoc Ignacio aka Nacho.

“Discover what is to be taken seriously and laugh at the rest,” Herman Hesse.

Want more examples of rad science videos making a splash? Check out

ASAP Science | Dr. Carin Bondar’s Wild Sex (Biology Series) | TED-Ed

A bit of what I’ve been up to at MIT

I recently moved to Cambridge, MA to take the best job of all time helping Sebastian Seung’s Computational Neuroscience Lab at MIT build a game to map the human brain. Yea. It’s called EyeWire and you should check it out.

Best job in the world for several reasons. For someone obsessed with thinking about thinking, this life is positively dreamy.  I think I live one solid series of awesome moments. I love the people in my lab. I got to move to Cambridge and live one mile from Harvard and one mile from MIT. I love walking to work. I love work! It doesn’t feel like a job. I love going to hackathons. I love hanging out with geniuses. I love MIT Media Lab. I love working with neuroscientists. I love learning new things. I love being around curious people ready to share their passion for creating game-changing technologies. I love going to intellectual events at MIT and Harvard. I love connecting with so many TEDxers on the east coast! I love snow (though we haven’t had much yet). I love great food and even greater company. I love talking about molecules and python and infographics and chilling with scientists every day. Bascially, I love life. I love life very much.

I’m aslo helping a group at the Media Lab (which I’m not really supposed to talk about), developing a new app for TEDx music (also not supposed to talk about..but no one reads this blog ;) and building an anonymized open-source database of health and lifestyle data with WIkiLife. Other things too..but it’s late and I want to read Nietzsche.

Below is a post I just wrote for the EyeWire blog. I blog at MIT now. Rad. Life is amazing. I hope you, dear reader, are following you passion and pursuing diligently the ideas that strike you most curious.  Reality will exceed your wildest expectations if you let it.

Cheers, much love.

Amy

It may come as a surprise that although we know much about how the eye works, neuroscience researchers do not fully understand how visual signals translate into perception.

We’ve landed on Mars, can grow organs, and even skydive from space, yet when it comes to a thorough understanding of the territory so close to home that it is home, much is missing. Neuroscientists don’t even know precisely how many different types of cells are in the brain. Here at Sebastian Seung‘s Computational Neuroscience Lab at MIT, we’re taking a different approach: crowd-sourcing. In order to solve the mind’s great mysteries, we need you.

Why don’t we know how the mind works? One reason is that your mind is massive. Researchers estimate that there are 100 billion neurons in your brain with about one million miles of connectivity. A million miles is equivalent to driving around Earth 40 times. You can infer that in order for such great length of neurons to fit into your three micron scale image by FSUpound brain these structures must be very tiny. A large neuron is about 100 microns in diameter while the contact area of a synapse is about 400 nm in length.

In order to see neurons and the tiny structures called dendrites through which they function, researchers utilize a new imaging technique. “Fix whole brain tissue, slice off layers just a few microns thick, image each slice with an electron microscope, and trace the path of each neuron,” explains David Zhou, Masters Student at Carnegie Mellon, on Quora. These gamechanging techniques generate terabytes of data for even a cubic milimeter of brain tissue. Now that we can see the brain at the synaptic scale, we have to analyze the images. How?

neuron cell reconstruction Seung Lab

The image above shows the process of layering image slices to render 3D reconstructions. Like most neuroscience labs, the Seung Lab uses a combination of AI algorithms and tracing (3D reconstruction) performed by humans. Why not just use algorithms? Images can be challenging to identify, particularly for a computer. Pure algorithms make many mistakes, such as slicing a single cell into thousands of pieces and merging multiple cells into one monstrously massive neuron. See below image for an example of AI missing a chunk of a neuron.

correcting a computer's mistake, Seung Lab

We hope to one day train computers to map neurons on their own; however, that day will be far in the future and we need to accelerate neuroscience discovery now. To achieve this, we need something more intelligent than even the most powerful supercomputer— you.

It takes an MIT-trained neuroscientist anywhere from 15 to 80 hours to reconstruct a single neuron. At that rate, it would take about 570,000,000 years to map the connectivity of an enture human brain, known as aconnectome. This is why we need your help.

Rather than mapping and entire brain, we’re starting with a retina. Our goal is to map the connections of a specific type of cell: J-Cells. These neurons are responsible for perception of upward motion. We plan to publish the outcome in a scientific journal and list EyeWire users as co-authors.

By playing the 3D game Eyewire, you become part of the Seung Lab at MIT by helping to map the connections of a neural network.

Scientific American writes that “no specialized knowledge of neuroscience is required [to play EyeWire]; citizen scientists need only be curious, intelligent and observant. Your input will help scientists understand how the retina functions. It will also be used by engineers to improve the underlying computational technology, eventually making it powerful enough to detect “miswirings” of the brain that are hypothesized to underlie disorders like autism and schizophrenia.”

We hope that you will help us trace the wires of perception through EyeWire. Play EyeWire and let us know what you think on Facebook.

Quantified Curiosity

I’m speaking today at Quantified Self Conference 2012 about Quantified Curiosity.  Below are my slides as well as some videos referenced in my talk. [UPDATE: presentation video now included]

Amy Robinson – QS Conference 2012 – Quantified Curiosity from Steven Dean on Vimeo.

Beautiful, scientific technological videos:






XVIVO Making the Complex Simple.

Want to play with the data?  Email me amyleerobinson at gmail dot com.

Gephi Graph of Ideas PDF.

Healthy Mind (and what is a mind, anyway?) Presentation

Yesterday’s presentation at Redstone Federal Arsenal about building a healthy mind also touched on how a mind comes about from matter.  The slides below have been reformatted with added text to be more slideshare friendly.

Presentation opened with a video by XVIVO Scientific Animation used with permission.  Copyright prevents embedding but you can watch it here.

Slides:

Presentation ended with Jason Silva’s The Biological Benefits of Being Awestruck:

The Biological Advantage of Being Awestruck – by @Jason_Silva from Jason Silva on Vimeo.

Enjoy!