A University of Rochester Medical Center research team, whose work could advance understanding of disorders like schizophrenia and autism, has won the largest National Institutes of Health award ever scored by UR’s Brain and Cognitive Sciences Department.
The five-year, $12.2 million grant supports the scientists’ efforts to unspool brain processes that help us interpret ambiguous sensory inputs. The award is part of the NIH’s Brain Research through Advancing Innovative Neurotechnologies Initiative. BRAIN aims to develop and better understand cutting-edge technologies to aid researchers seeking new ways to treat, cure and possibly prevent brain disorders.
Disorders like schizophrenia and autism are thought to at least in part stem from sufferers’ brains being overloaded with visual and auditory signals or scrambling them. The questions the URMC team is trying to answer have to do with how human brains normally process ambiguous sensory data to assemble an accurate picture of the outside world.
Led by Brain and Cognitive Sciences professors Greg DeAngelis and Ralf Haefner, the team is looking at how brain circuits untangle sensory inputs that aren’t immediately clear.
A person seated in a train that isn’t moving suddenly sees a train on an adjacent track begin to move, for example. For an instant, it is not clear to the person on the stationary train which train is moving and which is not.
Most of us quickly sort it out. But how do we do it? The brain employs a process called causal inference in which it sorts through various possible outcomes to arrive at the most likely explanation and then forms a visual or auditory image that correlates closely enough to the external event for us to orient ourselves in space.
Statisticians and epidemiologists use complex mathematical formulas and arcane calculations to work out causal inference problems in their disciplines. To work out problems like which train is moving and which is not, our brains do similar calculations.
Most of the time for most of us, this process “works so well … we take for granted what a difficult computational problem it is,” DeAngelis says. Different parts of the brain process different stimuli from different sensory organs. Sensory signals themselves, meanwhile, are not precise but are “noisy and incomplete.”
Yet somehow, even though we have no conscious awareness of the calculations our brains instantaneously make, an accurate enough picture of the external world emerges for us to move about, often without colliding with stationary or moving objects or other people.
DeAngelis and his team are applying data analyses, lab experiments and cognitive theory to the problem in an attempt to map out a more precise picture of how individual neurons and networks of neurons form and interact to arrive at a usable image of the world.
In addition to possible applications to development of treatments for schizophrenia or autism, such research could point the way to better artificial intelligence algorithms to help guide aircraft autopilot systems or self-driving cars, Haefner says.
Says DeAngelis, while answers to the fundamental cognitive science questions his team is probing will not directly produce treatments for brain disorders or new AI algorithms, they could provide a roadmap for others to follow.
Will Astor is Rochester Beacon senior writer.