Growth chart networks have now been identified to serve as measure of dysfunction and sustained attention in young teens. Results from a study by the University of Michigan Medical School, published on April 13, 2016, in the Journal of the American Model Association Psychiatry (JAMA), revealed that those suffering from attention difficulties have underdeveloped and dull growth chart networks. These patterns of deviation from normative maturational trajectories signified shallow maturation with reduced attention performance. Parallel associations were found to exist between intrinsic connectivity network (ICN) dysmaturation and diagnosis of attention-deficit hyperactivity disorder.
The study included 519 participants who were selected out of 9,498 individuals with a mean age of 15.7 years. All participants went through genetic and brain imaging testing regarding their memory and thinking as part of the Philadelphia Neurodevelopmental Cohort at University of Pennsylvania. They were then shown a sequence of letters and numbers on a computer screen using a standardized test which monitored their attention functioning and compared their brain scans to it. 25 individuals (4.8 percent) out of these fulfilled the criteria for attention deficit/hyperactivity disorder and were found to be the furthest of the curve of brain network development.
The growth chart networks that were created bore close resemblance to intrinsic connectivity networks which are essential for the function of brain organization. Healthy attention is formed through the interaction of these networks. The purpose of these networks are to handle those tasks that are cognitively demanding as well as the default mode network which focuses on the inwardly thoughts we experience, such as when daydreaming. As adults, it is easier for the brain to distinguish between these two modes and switch from one to the other but as young teens this can get confusing causing disruption and possible behavior or cognitive problems.
“The human brain is organized into several large-scale intrinsic connectivity networks, each associated with distinct neurocognitive functions. Moreover, relationships within and between (intrinsic connectivity networks) exhibit clear trajectories of change from childhood to young adulthood. It is plausible, then, that deviations from normative trajectories of network maturation might be predictive of a range of clinically important psychological characteristics and conditions.”
Chandra Sripada, a psychiatrist and author of the study, says, “Growth charts enable a family and their physician to quickly spot problematic development, and when necessary, intervene appropriately.” He further goes on to say, “These brain network growth charts show real promise but they are far away from being ready for clinical use.”
Further research done on growth charts of brain networks could lead to a confirmed diagnosis of ADHD for children with enhanced treatment methods helping the child cope at home as well as in the school.
The current research focused on MRI imaging to gather results and the complexities and expenses involved with neuroimaging serve as an obstacle for its use as a screening tool. The need for less expensive techniques such as electroencephalography (EEG) to find additional information during the process of tracking network maturity is a target in the near future.
Authors of the study conclude by saying, “This study introduces a novel brain network growth charting method for the prediction of attention impairment. Our results invite further investigation into the use of neuroimaging to identify patterns of brain dysmaturation that can serve as early, objective markers of cognitive problems and disorder vulnerability.”
A study titled ‘Adolescent Brain Cognitive Development study’ (ABCD) is currently underway involving 10,000 teens to test the usefulness of the growth charting method. “The ABCD study is unprecedented in size and provides a real chance to develop definitive growth charts for brain networks. We have the opportunity to understand how brain network development relates to a variety of outcomes, including cognition, emotion, personality, and behavior,”states Sripada. The research is being led by Mary Heitzeg and Robert Zucker and is funded by the National Institutes of Health, the Center for Computational Medicine and the John Templeton Foundation.