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computational neuroscience

The Amazing Brain: Visualizing Data to Understand Brain Networks

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The NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative continues to teach us about the world’s most sophisticated computer: the human brain. This striking image offers a spectacular case in point, thanks to a new tool called Visual Neuronal Dynamics (VND).

VND is not a camera. It is a powerful software program that can display, animate, and analyze models of neurons and their connections, or networks, using 3D graphics. What you’re seeing in this colorful image is a strip of mouse primary visual cortex, the area in the brain where incoming sensory information gets processed into vision.

This strip contains more than 230,000 neurons of 17 different cell types. Long and spindly excitatory neurons that point upward (purple, blue, red, orange) are intermingled with short and stubby inhibitory neurons (green, cyan, magenta). Slicing through the neuronal landscape is a neuropixels probe (silver): a tiny flexible silicon detector that can record brain activity in awake animals [1].

Developed by Emad Tajkhorshid and his team at University of Illinois at Urbana-Champaign, along with Anton Arkhipov of the Allen Institute, Seattle, VND represents a scientific milestone for neuroscience: using an adept software tool to see and analyze massive neuronal datasets on a computer. What’s also nice is the computer doesn’t have to be a fancy one, and VND’s instructions, or code, are publicly available for anyone to use.

VND is the neuroscience-adapted cousin of Visual Molecular Dynamics (VMD), a popular molecular biology visualization tool to see life up close in 3D, also developed by Tajkhorshid’s group [2]. By modeling and visualizing neurons and their connections, VND helps neuroscientists understand at their desktops how neural networks are organized and what happens when they are manipulated. Those visualizations then lay the groundwork for follow-up lab studies to validate the data and build upon them.

Through the Allen Institute, the NIH BRAIN Initiative is compiling a comprehensive whole-brain atlas of cell types in the mouse, and Arkhipov’s work integrates these data into computer models. In May 2020, his group published comprehensive models of the mouse primary visual cortex [3].

Arkhipov and team are now working to understand how the primary visual cortex’s physical structure (the cell shapes and connections within its complicated circuits) determines its outputs. For example, how do specific connections determine network activity? Or, how fast do cells fire under different conditions?

Ultimately, such computational research may help us understand how brain injuries or disease affect the structure and function of these neural networks. VND should also propel understanding of many other areas of the brain, for which the data are accumulating rapidly, to answer similar questions that still remain mysterious to scientists.

In the meantime, VND is also creating some award-winning art. The image above was the second-place photo in the 2021 “Show us Your BRAINs!” Photo and Video Contest sponsored by the NIH BRAIN Initiative.

References:

[1] Fully integrated silicon probes for high-density recording of neural activity. Jun JJ, Steinmetz NA, Siegle JH, Denman DJ, Bauza M, Barbarits B, Lee AK, Anastassiou CA, Andrei A, Aydın Ç, Barbic M, Blanche TJ, Bonin V, Couto J, Dutta B, Gratiy SL, Gutnisky DA, Häusser M, Karsh B, Ledochowitsch P, Lopez CM, Mitelut C, Musa S, Okun M, Pachitariu M, Putzeys J, Rich PD, Rossant C, Sun WL, Svoboda K, Carandini M, Harris KD, Koch C, O’Keefe J, Harris TD. Nature. 2017 Nov 8;551(7679):232-236.

[2] VMD: visual molecular dynamics. Humphrey W, Dalke A, Schulten K. J Mol Graph. 1996 Feb;14(1):33-8, 27-8.

[3] Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex. Billeh YN, Cai B, Gratiy SL, Dai K, Iyer R, Gouwens NW, Abbasi-Asl R, Jia X, Siegle JH, Olsen SR, Koch C, Mihalas S, Arkhipov A. Neuron. 2020 May 6;106(3):388-403.e18

Links:

The Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)

Models of the Mouse Primary Visual Cortex (Allen Institute, Seattle)

Visual Neuronal Dynamics (NIH Center for Macromolecular Modeling and Bioinformatics, University of Illinois at Urbana-Champaign)

Tajkhorshid Lab (University of Illinois at Urbana-Champaign)

Arkhipov Lab (Allen Institute)

Show Us Your BRAINs! Photo & Video Contest (BRAIN Initiative/NIH)

NIH Support: National Institute of Neurological Disorders and Stroke


Creative Minds: Reverse Engineering Vision

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Networks of neurons in the mouse retina

Caption: Networks of neurons in the mouse retina. Green cells form a special electrically coupled network; red cells express a distinctive fluorescent marker to distinguish them from other cells; blue cells are tagged with an antibody against an enzyme that makes nitric oxide, important in retinal signaling. Such images help to identify retinal cell types, their signaling molecules, and their patterns of connectivity.
Credit: Jason Jacoby and Gregory Schwartz, Northwestern University

For Gregory Schwartz, working in total darkness has its benefits. Only in the pitch black can Schwartz isolate resting neurons from the eye’s retina and stimulate them with their natural input—light—to get them to fire electrical signals. Such signals not only provide a readout of the intrinsic properties of each neuron, but information that enables the vision researcher to deduce how it functions and forges connections with other neurons.

The retina is the light-sensitive neural tissue that lines the back of the eye. Although only about the size of a postage stamp, each of our retinas contains an estimated 130 million cells and more than 100 distinct cell types. These cells are organized into multiple information-processing layers that work together to absorb light and translate it into electrical signals that stream via the optic nerve to the appropriate visual center in the brain. Like other parts of the eye, the retina can break down, and retinal diseases, including age-related macular degeneration, retinitis pigmentosa, and diabetic retinopathy, continue to be leading causes of vision loss and blindness worldwide.

In his lab at Northwestern University’s Feinberg School of Medicine, Chicago, Schwartz performs basic research that is part of a much larger effort among vision researchers to assemble a parts list that accounts for all of the cell types needed to make a retina. Once Schwartz and others get closer to wrapping up this list, the next step will be to work out the details of the internal wiring of the retina to understand better how it generates visual signals. It’s the kind of information that holds the key for detecting retinal diseases earlier and more precisely, fixing miswired circuits that affect vision, and perhaps even one day creating an improved prosthetic retina.


Snapshots of Life: A Colorful Look Inside the Retina

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Mapping neurons in the retina

Credit: Amy Robinson, Alex Norton, William Silversmith, Jinseop Kim, Kisuk Lee, Aleks Zlasteski, Matt Green, Matthew Balkam, Rachel Prentki, Marissa Sorek, Celia David, Devon Jones, and Doug Bland, Massachusetts Institute of Technology, Cambridge, MA; Sebastian Seung, Princeton University, Princeton, NJ

This eerie scene might bring back memories of the computer-generated alien war machines from Steven Spielberg’s War of the Worlds thriller. But what you’re seeing is a computer-generated depiction of a quite different world—the world inside the retina, the light-sensitive tissue that lines the back of the eye. The stilt-legged “creatures” are actually ganglion nerve cells, and what appears to be their long “noses” are fibers that will eventually converge to form the optic nerve that relays visual signals to the brain. The dense, multi-colored mat near the bottom of the image is a region where the ganglia and other types of retinal cells interact to convey visual information.

What I find particularly interesting about this image is that it was produced through the joint efforts of people who played EyeWire, an internet crowdsourcing game developed in the lab of computational neuroscientist Sebastian Seung, now at Princeton University in New Jersey.  Seung and his colleagues created EyeWire using a series of high-resolution microscopic images of the mouse retina, which were digitized into 3D cubes containing dense skeins of branching nerve fibers. It’s at this point where the crowdsourcing came in. Online gamers—most of whom aren’t scientists— volunteered for a challenge that involved mapping the 3D structure of individual nerve cells within these 3D cubes. Players literally colored-in the interiors of the cells and progressively traced their long extensions across the image to distinguish them from their neighbors. Sounds easy, but the branches are exceedingly thin and difficult to follow.


Creative Minds: Meet a Theoretical Neuroscientist

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Sean Escola

Sean Escola

Most neuroscientists make their discoveries in a traditional laboratory or clinical setting. Sean Escola, a theoretical neuroscientist at Columbia University in New York, just needs a powerful computer and, judging from his photo, a good whiteboard.

Using data that he and his colleagues have recorded from living brain cells, called neurons, Escola crunches numbers to develop rigorous statistical models that simulate the activity of neuronal circuits within the brain. He hopes his models will help to build a new neuroscience that brings into sharper focus how the brain’s biocircuitry lights up to generate sensations and thoughts—and how it misfires in various neurological disorders, particularly in mental illnesses.