New clues to cracking brain's neural code

Decoding the complex electrical signals that brain cells use to "talk" to each other is a new and important frontier in neuroscience, one that could revolutionize the diagnosis and treatment of neurological and psychiatric disease.

Now, a multicenter team, led by a researcher at Weill Cornell Medical College in New York City, says they have uncovered a vital clue to help decode that neural language.

The groundbreaking work is published in Nature.

"We discovered that the specific timing of these electrical pulses is crucial to interpreting how the neural code works as the brain represents what it sees in the natural environment. Understanding the 'time scales' that matter to the brain gives us insight into which units of the neural code we need to focus on if we ever hope to decode it," explains lead author Dr. Daniel A. Butts, who is an Institute Fellow and instructor of computational neuroscience at the HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine at Weill Cornell.

The term "neural code" may be unfamiliar to most people, but it underlies nearly everything the brain's trillions of cells do each millisecond.

"The neural code is the key to understanding the patterns of electrical impulses that neurons use to communicate. These electrical patterns allow the brain to make sense of incoming stimuli, make decisions based on that information, and coordinate its activities to carry out tasks," Dr. Butts explains.

Trouble is, right now scientists have no way of interpreting this neural language.

"It's like we're hearing Morse code, but have no training in understanding what the separate beeps and dashes mean," Dr. Butts says. "And the brain's neural code is infinitely more complex than Morse code."

Unraveling the neural code would undoubtedly be a major milestone for science.

"Imagine -- if we knew what specific signals meant, we might very easily diagnose brain illnesses, based on those electrical signatures. We could also track a patient's recovery, much like cardiologists track the health of heart patients using EKGs," Dr. Butts says. "Understanding the neural code would also greatly aid in psychiatric drug research; we could understand a medicine's effect in the brain based on its known effects on its electrical activity, for example. And then there's the field of 'neural prosthetics' -- building direct links between the brain and machines that might allow the paralyzed to regain movement or the blind to see."

Unfortunately, right now, most of the neural code is just so much "static" to even the most learned neuroscientists.

This new work -- which Dr. Butts began when he worked at Harvard University in collaboration with researchers at SUNY College of Optometry and completed at Weill Cornell -- sought to determine a crucial piece of the puzzle.

"If you were trying to decipher Morse code, you'd first want to know which units were important to focus on -- Is it the individual beeps? The series of beeps? -- and which gaps between beeps signaled that a new unit of the language was present," he explains. "That's what this paper was all about."

In their work, Dr. Butts and collaborators at Harvard and SUNY School of Optometry focused on the visual system in an animal model. The researchers focused on the visual system because it is well-studied and because it is relatively easy to match up stimuli (i.e., images) with specific neural code responses.

The team also added a new element to the experiment. "In most prior work, scientists had simply presented the animal with basic shapes such as lines and dots that evolve with simple dynamics and then tracked the resulting pattern of electrical activity in the brain," Dr. Butts says. "But we thought that the brain would work differently under 'real world' conditions."

So, in these new experiments, electrical activity in the lateral geniculate nucleus (LGN) -- a key vision center in the mammalian brain -- was recorded as the brain reacted during the viewing of a special movie.

"The movie was actually made by a group in Germany using a camera mounted on the head of a freely roaming cat as it made its way through the forest," Dr. Butts explains. "In that sense, it's much closer to real-life stimuli changing over time."

This change in the study protocol brought about exciting new findings. "Looking at the timing of electrical impulses in the LGN, we found that the results we got in the context of the 'natural movies' was very different than those from simpler laboratory experiments," Dr. Butts says. "We believe this goes much farther to reveal the true language the brain is using in everyday life."

Specifically, the group learned that to interpret the neural code correctly, scientists must look at signaling using roughly 10-millisecond increments.

"Using the Morse code analogy, this is like discovering that a 'short gap' separates the letters in the code and a 'long gap' separates the words," he explains.

The experiment now gives researchers a place to start as they parse out the separate units of the neural code -- a major achievement that merited the study's inclusion in a prestigious journal such as Nature.

"And because the LGN sends signals directly to the cortex -- the part of the brain unique to mammals where higher-level processes such as planning and decision-making occur -- decoding signaling in that area is a logical next step," Dr. Butts says.

He stresses that this research is still in its infancy. But once the neural code is "cracked," the possibilities for advancing neuroscience may be limitless.

"Being able to accurately interpret the brain's activity is going to be one of the great accomplishments in science," Dr. Butts says. "This is an important step in that direction."

This work was funded by a Charles King Trust Postdoctoral Fellowship (Bank of America, Co-Trustee, Boston), by the NGIA, by the NIH and by the SUNY Research Foundation.

Co-researchers include senior researcher Dr. Garrett B. Stanley of Harvard University, Cambridge, Mass.; Dr. Jianzhong Jin, Dr. Jose-Manuel Alonso and Chong Weng of the State University of New York College of Optometry, New York City; Dr. Chun-I Yeh of SUNY College of Optometry and the University of Connecticut, Storrs; and Dr. Nicolas Lesica of Harvard University.