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Increased ‘Blurring’ of Brain Networks May Contribute to Poor Memory
Nov. 5, 2014
Dr. Gagan Wig
People may be inclined to think that poor memory is associated with a gradual disconnecting of the brain’s circuitry, but can too much connectivity in the brain actually play a role in worsening memory?
New research from the Center for Vital Longevity (CVL) at UT Dallas suggests it may.
The study, published this week in the Proceedings of the National Academy of Sciences, has found that the more connections forged between a brain’s sub-networks, the poorer a person’s memory was.
Using functional MRI brain scans, Dr. Gagan Wig and his colleagues examined how brain networks are composed of segregated sub-networks that mediate specialized functions in young and older adults.
“Different areas of the brain mediate specialized functions, and brain areas that have similar functions tend to be highly connected with one another,” said Wig, an assistant professor in the School of Behavioral and Brain Sciences at UT Dallas.
“The pattern of connectivity results in the formation of communities or sub-networks similar to people who interact in a social network. You tend to be highly connected to a few subgroups of closer friends and less connected to other individuals.”
Based on scans and cognitive testing in 210 adults ages 20 to 89, individuals whose brains had less segregation of specialized sub-networks, regardless of age, tended to have poorer memory.
Researchers found that as adults age the networks in the brain become less distinct, less specialized and less segregated.
Younger adults whose brains had more segregation had better memory ability, and similarly, older adults who had higher segregation had better memory ability, the research team found.
They also discovered that increasing age is associated with decreased segregation of brain sub-networks.
“Younger persons exhibit many intra-network connections for specialized processing of specific tasks, while actually having sparser inter-network connections that aid communication between networks, keeping them distinct,” Wig said. “But in some adults the picture blurs as they age, with the brain networks becoming progressively less distinct, less specialized and less segregated between the networks that make up the system as a whole.”
The work uses a different approach in looking how the brain operates on a network level, and may eventually lead to new clinical diagnostic criteria for age-related memory disorders — criteria that also may help predict pathological decline, Wig said.
To create a new measure of interconnectivity and efficiency on a global scale, Wig’s lab used an area of mathematics called graph theory to characterize the segregation of brain networks. This approach has been used to study social media networks, such as Facebook, the Internet, and the flow of public transportation, disease transmission, and even outbreaks of contagion.
Previous work in the field has largely focused on describing age-related differences in function at the level of activity in individual brain areas.
A natural next step will be to understand how the segregation of brain sub-networks in each individual changes as they grow older, by scanning participants repeatedly over time and measuring their brain network properties.
The latest findings stem from data collected by the CVL’s Dallas Lifespan Brain Study.
Micaela Chan, a graduate student at UT Dallas and member of Wig’s lab at CVL, was the study’s lead author. UT Dallas’ Dr. Denise C. Park, director of research at CVL, and Neil K. Savalia, a research assistant in Wig’s lab, and Dr. Steven Petersen of Washington University in St. Louis were co-authors of the study.
Funding was provided by the National Institute on Aging, part of the National Institutes of Health.
Media Contact: Office of Media Relations, UT Dallas, (972) 883-2155, [email protected].