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Growth Charting May Aid in Early Identification of ADHD


A first-of-its-kind study applies traditional growth-charting to evaluate abnormal developmental patterns in connectivity networks of a child's brain.

Growth charting using brain scanning techniques may be a way to identify abnormal development of brain networks in attention deficit hyperactivity disorder (ADHD), according to a study published in the May issue of JAMA Psychiatry.

The study is the first to use growth charting methods to evaluate abnormal patterns that emerge during development in intrinsic connectivity networks (ICNs) of the brain. The findings have potential application in the development of neuroimaging biomarkers for other pychiatric conditions.

“In our resting-state imaging study of 519 youth in the Philadelphia Neurodevelopmental Cohort, we used a novel network growth charting method. 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,” wrote lead author Chandra Sripada, MD, PhD, of the University of Michigan, and colleagues.

ICN   Intrinsic connectivity network

FPN   Frontoparietal control network

DAN  Dorsal attention network

VAN   Ventral attention network

DMN   Default mode network

The frontoparietal control network (FPN) is thought to be involved in controlling attention. Abnormalities in certain ICNs in this network have been implicated in ADHD, particularly the dorsal attention network (DAN, involved in goal-directed attention), the ventral attention network (VAN, involved in transient shifts in attention), and the default mode network (DMN, which is activated at rest and less active during task-related activity). The balance between these networks may play a role in sustained attention.

ICNs mature substantially during youth, though whether changes in ICN maturation can reliably predict attentional problems has been an open question.

In the study, researchers used a network growth charting approach modeled after methods that have been used for 200 years to measure characteristics like growth in height, weight, and head circumference, and to aid in earlier identification of childhood disorders.

Researchers used data from the Philadelphia Neurodevelopmental Cohort, a prospective population-based sample of 9498 youth aged 8-22 years that combines neuroimaging with neuropsychological and clinical data.

Using resting-state brain MRI, researchers constructed a “connectome” of functional connectivity for 15 brain network components. To construct a normal growth chart, they mapped how these components change during development in a healthy cohort. Next, they developed a maturational deviation score, to assess how each child’s personal connectome compares to age-expected values for normal development. Finally, they evaluated how well a child’s maturational deviation scores aligned with scores on the Penn Continuous Performance Test (which was age-corrected for accuracy and measures sustained attention).

The analysis included 519 youths (mean age 15.7 years, 43% male) of whom 25 (4.8%) met criteria for ADHD.

Results, Discussion

The authors noted that ADHD was diagnosed using an abbreviated clinical interview, so the results may not apply to youth diagnosed using standard clinical methods.

“Many psychiatric disorders are thought to have their origins in early neurodevelopmental events. 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,” they concluded.

In a linked editorial Philip Shaw, BM, BCh, PhD, of the National Institute of Mental Health (Bethesda, MD),  commented that the idea of using growth charts to measure brain maturation may seem “far-fetched” but “[T]he study by Kessler and colleagues joins other recent work as tentative steps in this direction.”

He pointed out that the authors only evaluated linear change. Other variables in growth like height and weight have a snakelike growth pattern and brain growth may be just as complex. Larger, prospective studies will be needed to tease out these nuances and evaluate inter-individual variation.

“Inferring developmental processes from cross-sectional data is often the only option, and this study exemplifies how elegantly this can be done. Nonetheless, the inherent limitations of cross-sectional data mean that definitive growth curves will require longitudinal observations,” he wrote.

He also highlighted that the maturational deviation score represented 25% of the difference in sustained attention skills. By comparison, general intelligence represented just 7% of the difference.

“The finding represents an impressive validation of how patterns of functional connectivity can pertain to a core cognitive skill. 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,” he emphasized.

Take Home Points

  • Study suggests that growth charting using brain scanning techniques may be a way to identify abnormal development of brain networks in ADHD
  • Using resting-state brain MRI, researchers constructed a growth chart that maps how 15 brain network components change during development in a healthy cohort, and compared how deviations in maturation align with attentional scores in 519 youth
  • The model significantly predicted ADHD; divergence from normal growth trajectories was a reliable biomarker of severe attention impairment and problems with ICN maturation were linked to diagnosis of ADHD
  • Larger, longitudinal studies are needed to confirm these findings




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