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neurology

A Scientist and Conservation Photographer

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These stunning images of animals were taken by Susan McConnell, whose photographs have appeared in Smithsonian Magazine, National Geographic, Nature’s Best Photography, Africa Geographic, and a number of other publications. But photography is just part of her professional life. McConnell is best known as a developmental neurobiologist at Stanford University, Palo Alto, CA, and an elected member of the U.S. National Academy of Sciences.

How did McConnell find the time while tracing the development of the brain’s biocircuitry to launch a second career as a nature photographer? Her answer: Every research career has its seasons. When McConnell launched her lab in 1989 at the age of 31, she was up to her eyeballs recruiting staff, writing research grants, and pursuing many different leads in her quest to understand how neurons in the brain’s cerebral cortex are produced, differentiated, and then wired together into functional circuits.


Brain in Motion

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Credit: Itamar Terem, Stanford University, Palo Alto, CA, and Samantha Holdsworth, University of Auckland, New Zealand

Though our thoughts can wander one moment and race rapidly forward the next, the brain itself is often considered to be motionless inside the skull. But that’s actually not correct. When the heart beats, the pumping force reverberates throughout the body and gently pulsates the brain. What’s been tricky is capturing these pulsations with existing brain imaging technologies.

Recently, NIH-funded researchers developed a video-based approach to magnetic resonance imaging (MRI) that can record these subtle movements [1]. Their method, called phase-based amplified MRI (aMRI), magnifies those tiny movements, making them more visible and quantifiable. The latest aMRI method, developed by a team including Itamar Terem at Stanford University, Palo Alto, CA, and Mehmet Kurt at Stevens Institute of Technology, Hoboken, NJ. It builds upon an earlier method developed by Samantha Holdsworth at New Zealand’s University of Auckland and Stanford’s Mahdi Salmani Rahimi [2].


Measuring Brain Chemistry

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Anne Andrews

Anne Andrews
Credit: From the American Chemical Society’s “Personal Stories of Discovery”

Serotonin is one of the chemical messengers that nerve cells in the brain use to communicate. Modifying serotonin levels is one way that antidepressant and anti-anxiety medications are thought to work and help people feel better. But the precise nature of serotonin’s role in the brain is largely unknown.

That’s why Anne Andrews set out in the mid-1990s as a fellow at NIH’s National Institute of Mental Health to explore changes in serotonin levels in the brains of anxious mice. But she quickly realized it wasn’t possible. The tools available for measuring serotonin—and most other neurochemicals in the brain—couldn’t offer the needed precision to conduct her studies.

Instead of giving up, Andrews did something about it. In the late 1990s, she began formulating an idea for a neural probe to make direct and precise measurements of brain chemistry. Her progress was initially slow, partly because the probe she envisioned was technologically ahead of its time. Now at the University of California, Los Angeles (UCLA) more than 15 years later, she’s nearly there. Buoyed by recent scientific breakthroughs, the right team to get the job done, and the support of a 2017 NIH Director’s Transformative Research Award, Andrews expects to have the first fully functional devices ready within the next two years.


Unlocking the Brain’s Memory Retrieval System

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Memory Trace in Mouse Hippocampus

Credit:Sahay Lab, Massachusetts General Hospital, Boston

Play the first few bars of any widely known piece of music, be it The Star-Spangled Banner, Beethoven’s Fifth, or The Rolling Stones’ (I Can’t Get No) Satisfaction, and you’ll find that many folks can’t resist filling in the rest of the melody. That’s because the human brain thrives on completing familiar patterns. But, as we grow older, our pattern completion skills often become more error prone.

This image shows some of the neural wiring that controls pattern completion in the mammalian brain. Specifically, you’re looking at a cross-section of a mouse hippocampus that’s packed with dentate granule neurons and their signal-transmitting arms, called axons, (light green). Note how the axons’ short, finger-like projections, called filopodia (bright green), are interacting with a neuron (red) to form a “memory trace” network. Functioning much like an online search engine, memory traces use bits of incoming information, like the first few notes of a song, to locate and pull up more detailed information, like the complete song, from the brain’s repository of memories in the cerebral cortex.


Teaching Computers to “See” the Invisible in Living Cells

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Brain Cell Analysis

Caption: While analyzing brain cells, a computer program “thinks” about which cellular structure to identify.
Credit: Steven Finkbeiner, University of California, San Francisco and the Gladstone Institutes

For centuries, scientists have trained themselves to look through microscopes and carefully study their structural and molecular features. But those long hours bent over a microscope poring over microscopic images could be less necessary in the years ahead. The job of analyzing cellular features could one day belong to specially trained computers.

In a new study published in the journal Cell, researchers trained computers by feeding them paired sets of fluorescently labeled and unlabeled images of brain tissue millions of times in a row [1]. This allowed the computers to discern patterns in the images, form rules, and apply them to viewing future images. Using this so-called deep learning approach, the researchers demonstrated that the computers not only learned to recognize individual cells, they also developed an almost superhuman ability to identify the cell type and whether a cell was alive or dead. Even more remarkable, the trained computers made all those calls without any need for harsh chemical labels, including fluorescent dyes or stains, which researchers normally require to study cells. In other words, the computers learned to “see” the invisible!


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