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Originally published February 26, 2014 at 8:02 PM | Page modified February 27, 2014 at 7:15 AM

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Allen Institute scientists try to decode mouse brain to understand our own

The Allen Institute sought to merge its semi-industrial data-gathering approach with more traditional science by hiring scientists from Harvard Medical School and the California Institute of Technology.


The New York Times

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When Clay Reid decided to leave his job as a professor at Harvard Medical School to become a senior investigator at the Allen Institute for Brain Science in Seattle in 2012, some of his colleagues congratulated him warmly and understood right away why he was making the move.

Others shook their heads. He was, after all, leaving one of the world’s great universities to go to the academic equivalent of an Internet startup, albeit an extremely well-financed, very ambitious one, created in 2003 by Paul Allen, a founder of Microsoft.

Still, “it wasn’t a remotely hard decision,” Reid said. He wanted to mount an all-out investigation of a part of the mouse brain. And although he was happy at Harvard, the Allen Institute offered not only great colleagues and deep pockets, but also an approach to science different from the classic university environment. The institute was already mapping the mouse brain in fantastic detail, and specialized in the large-scale accumulation of information in atlases and databases available to all of science.

Now, it was expanding, and trying to merge its semi-industrial approach to data gathering with more traditional science driven by individual investigators, by hiring scientists like Christof Koch from the California Institute of Technology as chief scientific officer in 2011 and Reid. As a senior investigator, he would lead a group of about 100 and work with scientists, engineers and technicians in other groups.

Without the need to apply regularly for federal grants, Reid could concentrate on one piece of the puzzle of how the brain works. He would try to decode the workings of one part of the mouse brain, the million neurons in the visual cortex, from, as he puts it, “molecules to behavior.”

There are many ways to map the brain and many kinds of brains to map. Although the ultimate goal of most neuroscience is understanding how human brains work, many kinds of research can’t be done on human beings, and the brains of mice and even flies share common processes with human brains.

The work of Reid, and scientists at Allen and elsewhere who share his approach, is part of a surge of activity in brain research as scientists try to build the tools and knowledge to explain — as well as can ever be explained — how brains and minds work. Besides the Obama administration’s $100 million Brain Initiative and the European Union’s $1 billion, decadelong Human Brain Project, there are numerous private and public research efforts in the United States and abroad, some focusing on the human brain, others like Reid’s focusing on nonhumans.

While the Human Connectome Project, which is spread among several institutions, aims for an overall picture of the associations among parts of the human brain, other scientific teams have set their sights on drilling to deeper levels.

For instance, the Connectome Project at Harvard is pursuing a structural map of the mouse brain at a level of magnification that shows packets of neurochemicals at the tips of brain cells.

At Janelia Farm, the Virginia research campus of the Howard Hughes Medical Institute, researchers are aiming for an understanding of the complete fly brain — a map of sorts, if a map can be taken to its imaginable limits, including structure, chemistry and genetics.

“I personally am inspired by what they’re doing at Janelia,” Reid said.

All these efforts start with maps and enrich them. If Reid is successful, he and his colleagues will add what you might call the code of a brain process, the language the neurons use to store, transmit and process information for this function.

Not that this would be any kind of final answer. In neuroscience, perhaps more than in most other disciplines, every discovery leads to new questions.

“With the brain,” Reid said, “you can always go deeper.”

As an undergraduate at Yale, Reid, 53, was steered toward the work of David Hubel and Torsten Wiesel, who had just won the Nobel Prize in 1981 for their work showing how binocular vision develops in the brain.

He read their work, and when he graduated in 1982, he was convinced that the study of the brain was both hard science and a wide-open field. He went on to an M.D.-Ph.D. program at Cornell Medical College and Rockefeller University, where Wiesel had his lab (he would go on to be president of Rockefeller).

After postdoctoral research in the Rockefeller lab, he stayed as a faculty member until moving to Harvard in 1996.

Intangible skills

Mathematics and physics were becoming increasingly important in neurobiology, a trend that has continued, but there was still a certain tension between different mindset, he recalled. He found that there were intangible skills involved in biological research. “Good biological intuition was equally important to chops in math and physics,” he said.

At Harvard, Reid worked on the Connectome Project to map the connections between neurons in the mouse brain. The Connectome Project aims at a detailed map, a wiring diagram at a level fantastically more detailed than the work being done to map the human brain with MRI machines. But electron microscopes produce a static picture from tiny slices of preserved brain.

Reid began working on tying function to mapping. He and one of his graduate students, Davi Bock, now at Janelia Farm, linked studies of active mouse brains to the detailed structural images produced by electron microscopes.

To crack the code of the brain, Reid said, two fundamental problems must be solved.

The first is: “How does the machine work, starting with its building blocks, cell types, going through their physiology and anatomy,” he said. That means knowing all the different types of neurons in the mouse visual cortex and their function — information that science doesn’t have yet.

It also means knowing what code is used to pass on information. When a mouse sees a picture, how is that picture encoded and passed from neuron to neuron? That is called neural computation.

“The other highly related problem is: How does that neural computation create behavior?” he said. How does the mouse brain decide on action based on that input?

He imagined the kind of experiment that would get at these deep questions. A mouse might be trained to participate in an experiment now done with primates in which an animal looks at an image. Later, seeing several different images in sequence, the animal presses a lever when the original one appears. Seeing the image, remembering it, recognizing it and pressing the lever might take as long as two seconds and involve activity in several parts of the brain.

Understanding those two seconds, Reid said, would mean knowing “literally what photons hit the retina, what information does the retina send to the thalamus and the cortex, what computations do the neurons in the cortex do and how do they do it, how does that level of processing get sent up to a memory center and hold the trace of that picture over one or two seconds.”

Then, when the same picture is seen a second time, “the hard part happens,” he said. “How does the decision get made to say, ‘That’s the one’?”

In pursuit of this level of understanding, Reid and others are gathering chemical, electrical, genetic and other information about what the structure of that part of the mouse brain is and what activity is going on.

They will develop electron micrographs that show every neuron and every connection in that part of a mouse brain. That is done on dead tissue. Then they will use several techniques to see what goes on in that part of the brain when a living animal reacts to different situations.

“We can record the activity of every single cell in a volume of cortex, and capture the connections,” he said.

With chemicals added to the brain, the most advanced light microscopes can capture movies of neurons firing. Electrodes can record the electrical impulses. And mathematical analysis of all that may decipher the code in which information is moved around that part of the brain.

Reid says solving the first part of the problem — receiving and analyzing sensory information — might be done in 10 years. An engineer’s precise understanding of everything from photons to action could be more on the order of 20 to 30 years away, and not reachable through the work of the Allen Institute alone. But, he wrote in an email, “the large-scale, coordinated efforts at the institute will get us there faster.”

He is studying only one part of one animal’s brain, but, he said, the cortex — the part of the mammalian brain where all this calculation goes on — is something of a general-purpose computer. So the rules for one process could explain other processes, like hearing. And the rules for decision-making could apply to many more complicated situations in more complicated brains. Perhaps the mouse visual cortex can be a kind of Rosetta stone for the brain’s code.

All research is a gamble, of course, and the Allen Institute’s collaborative approach, while gaining popularity in neuroscience, is not universally popular. Wiesel said it was “an important approach” that would “provide a lot of useful information.” But, he added, “it won’t necessarily create breakthroughs in our understanding of how the brain works.”

“I think the main advances are going to be made by individual scientists working in small groups,” he said.

Of course, in courting and absorbing researchers like Reid, the Allen Institute has been moving away from its broad data-gathering approach toward more focused work by individual investigators.

Bock, his former student, said his experience suggested that Reid had not only a passion and intensity for research, but a good eye for where science is headed as well.

“That’s what Clay does,” he said. “He is really good in that Wayne Gretzky way of skating to where the puck will be.”



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