Growth charting the brain’s networks could provide a novel way to detect neurocognitive abnormalities such as impaired attention functioning in youth, researchers said.
Resting-state functional neuroimaging of a small cohort of youth revealed deviations from normal patterns of brain maturation that predicted the diagnosis of attention-deficit/hyperactivity disorder (ADHD), Chandra Sripada, MD, PhD, of the University of Michigan in Ann Arbor, and colleagues reported online in JAMA Psychiatry.
“We mapped the normative maturational trajectories of major components of the functional connectome and showed that downshifted component expression relative to the normative profile (shallow maturation) is implicated in both impaired attention task performance and ADHD,” they wrote.
“Many psychiatric disorders are thought to have their origins in early neurodevelopmental events,” they added. “Our results invite further investigation into the use of network growth charting to identify patterns of brain dysmaturation that can serve as early, objective markers of cognitive problems and disorder vulnerability.”
The analysis showed that patterns of deviation from normative growth trajectories indicated sustained attention functioning. These patterns were a reliable biomarker of severe attention impairment, expressed as the peak receiver operating characteristic curve measured by area under the curve (79.3%), the researchers reported.
“In particular, a down-shifted pattern of intrinsic connectivity network (ICN) maturation (shallow maturation), rather than a right-shifted pattern (lagged maturation), was implicated in reduced attention performance,” Sripada and colleagues said. “Finally, parallel associations between ICN dysmaturation and diagnosis of attention-deficit/hyperactivity disorder were identified.”
This charting approach revealed the role of ICNs in sustained attention, they said. When individuals deviated from normative trajectories in the development of ICNs, they consistently exhibited worse sustained attention performance.
Other researchers have used functional connectomes — the coordinated patterns of brain activity, measured by functional magnetic resonance imaging — to predict sustained attention performance.
In the study, a total of 519 youths ages 8 to 22 were analyzed out of 1,000 who underwent brain imaging as part of a prospective, population-based sample of 9,498 youths in the Philadelphia Neurodevelopmental Cohort.
All participants had previously undergone genomic testing, neurocognitive assessment, and neuroimaging.
A total of 25 (4.8%) met criteria for attention-deficit/hyperactivity disorder. The mean age of the youth was 15.7 years, and 43% were male.
Data collection took place between Nov. 1, 2009, and Nov. 30, 2011, and data analysis was carried out between Feb. 1, 2015, and Jan. 15, 2016.
The investigators used the data to create maps of functional connectivity called components. The 15 components they identified varied in expression between participants, revealing a signature pattern of functional connectivity between and within brain networks.
By mapping how these components changed over time in the primarily healthy cohort, the researchers were able to create a normative growth curve and calculate how far each component’s expression deviated from age-expected values for a “maturational deviation score.”
This score accounted for approximately 25% of the variance in sustained attention skills, Philip Shaw, BM, BCh, PhD, of the National Human Genome Research Institute, said in an accompanying editorial. By comparison, general intelligence explained about 7% of the variance, he noted.
“The finding represents an impressive validation of how patterns of functional connectivity can pertain to a core cognitive skill,” Shaw wrote. “The question now arises of whether these functional alterations also contribute to the deficits in sustained attention found in other childhood disorders — not just ADHD.”
Since growth is a longitudinal process unique to each child, the cross-sectional data analysis used in this study, while performed “elegantly,” is “inherently limited,” Shaw said, adding that “definitive growth curves will require longitudinal observations.”
The diagnostic usefulness of a test “depends on many factors, but odds ratios in the double digits are usually needed,” he pointed out. And while “the further step to a specific diagnosis is less compelling,” this may not be a major drawback. “The goal of growth charts is not to clinch a specific diagnosis, but rather to prompt and inform further assessment.”
Translation of in vivo measures of the brain’s functional architecture into psychiatric growth charts “will be challenging,” warned Shaw, “but the conceptual and empirical foundations have been laid.”
Funding for this research was provided by the National Institutes of Health, the Center for Computational Medicine, and the John Templeton Foundation.
Shaw is funded by the Intramural Programs of the National Human Genome Research Institute and National Institute of Mental Health.
No conflicts of interest were disclosed.