[EDITOR’S NOTE: This is Jackie Xu’s first contribution to #sciencefriday. Jackie is a junior computer science major at MIT whose interests range from artificial intelligence to neuroscience. Welcome to the team, Jackie!]
Hello, world! Jackie, here. I’m happy to present my first post to #sciencefriday. What better way to start it off than with some new findings on our very own brain!
Our mysterious brain seamlessly coordinates amongst its many parts to do things like read this post. It takes a lot of computation and control to sit and read something for 10 minutes, say while listening to your favorite song on the radio, or while you subconsciously think about what you’re going to make for dinner tomorrow. The challenge that neuroscientists face today is to find more specific and reliable ways to measure these “abstract” neural functions like attention.
Typically, scientists have measured attention through behavioral tests and talking to individuals about their personal experiences, particularly when diagnosing attention disorders. Researchers at Yale University and the Yale School of Medicine are trying to turn people to look at the brain itself by defining a hopeful neuromarker for attention (Rosenberg, et. al., 2016).
Using functional magnetic resonance imaging (fMRI), which measures the flow of blood through the brain, the authors found unique indicators for attentiveness in actively-thinking and resting individuals. They define what they call the Sustained Attention Network (SAN) model, which allowed them to predict performance in attention for new individuals on tasks solely based on predefined neuromarkers that they observed through fMRI.
(Source: the paper’s authors!)
To test for attention, the researchers looked at the strength of signals sent between nodes scattered throughout each of the brain regions. These signals, when mapped onto a diagram of the brain’s functional regions, created a unique pattern and allowed the scientists to generate their model for attention.
The conclusions are that people who can maintain focus will have greater connections between their motor cortex (motor control), occipital lobes (vision), and cerebellum (motor control). People with attention issues will have connections between their temporal (sensory information and language) and parietal (sensory input & spatial awareness) lobes, with heightened connections within the temporal lobe and cerebellum.
If more researchers can replicate these results, the SAN model will be a big step towards more precise diagnostics for attention performance and disorders, removing the guesswork that psychiatrists face when diagnosing mental conditions like ADHD. Additionally, these findings bolster the idea that attention, like other neural functions, varies along a spectrum, rather than in discrete levels of ability. Thus, we can show through brain imaging that conditions like ADHD aren’t about the haves and the have-nots, but rather about how much.