Attention! Brain scans can tell if you are paying it

Date:
March 3, 2022
Source:
Yale University
Summary:
Using a model of fMRI data collected from 92 individuals performing
several types of attention-related tasks, researchers successfully
predicted how well those individuals would perform on the tasks
based on their brain scans alone. This generalized model can also
predict severity of an individual case of attention deficit and
hyperactivity disorder.

The study was published March 3 in the journal Nature Human
Behavior.



FULL STORY ==========================================================================
Data from brain scans can now answer an age-old question asked by parents
and teachers everywhere: Are you paying attention?

========================================================================== Using a model of fMRI data collected from 92 individuals performing
several types of attention-related tasks, the lab of Yale's Marvin Chun successfully predicted how well those individuals would perform on the
tasks based on their brain scans alone.

This generalized model can also predict severity of an individual case
of attention deficit and hyperactivity disorder.

The study was published March 3 in the journal Nature Human Behavior.

"Attention is such a fundamentally important ability for school, sports,
work, and even happiness, but it is hard to put a number on it like
blood pressure or IQ," said Chun, the Richard M. Colgate Professor of Psychology, professor of neuroscience, and co-corresponding author of
the paper. "Now we can put people in a scanner and get a score that
represents how well an individual will do on attention tasks relative
to other people." Attention has many dimensions, including the ability
to sustain attention or retain focus when distracted, and the capacity
to store upcoming tasks in working memory. For the new study, Chun and
a research team led by Yale's Kwangsun Ray Yoo distilled data taken from
brain scans of individuals as they performed a series of attention-related tasks, such as sustained focus exercises, and then linked that information
to patterns of activity across different brain regions. They then created
a computational model that it is so sensitive it can predict how well
an individual will perform on an attention- related task even when the
brain is resting.

"The brain is all interconnected, and is always running like a beating
heart," Chun said. "What we can do is take all those complex patterns
and analyze the data to create a fingerprint of the brain's ability to
pay attention." The measurement can help diagnose ADHD and be used as neurofeedback to help improve an individual's own focus.

The study builds on pioneering work conducted by Monica Rosenberg, a
co-author and former postdoctoral fellow at Yale who is now an assistant professor at the University of Chicago.

Chun is a member of the Wu Tsai Institute.


========================================================================== Story Source: Materials provided by Yale_University. Original written
by Bill Hathaway. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Kwangsun Yoo, Monica D. Rosenberg, Young Hye Kwon, Qi Lin, Emily W.

Avery, Dustin Sheinost, R. Todd Constable, Marvin M. Chun. A
brain-based general measure of attention. Nature Human Behaviour,
2022; DOI: 10.1038/ s41562-022-01301-1 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220303112243.htm

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