In Kids With ADHD, 3 Neural Networks Show Weaker-Than-Normal Interaction

ADHD
Interactions between three neural networks that help us pay attention are weaker than normal in children diagnosed with ADHD, suggesting a new method for diagnosis. goldsardine, CC by 2.0

Attention deficits can be seen in the brain, say researchers from Stanford University School of Medicine. Interactions between three neural networks are weaker than normal in children diagnosed with attention-deficit hyperactivity disorder, their new study indicates.

Even more, the degree of weakness correlated to the severity of each child’s symptoms, reported the researchers.

The outcome of this study “demonstrates that we can develop a very robust biomarker based on functional neuroimaging to reliably differentiate children with ADHD from other kids,” Dr. Weidong Cai, lead author and an instructor in psychiatry and behavioral sciences, said in a press release.

ADHD Nation

More than six million children in the United States (11 percent) have been diagnosed with ADHD, a disorder characterized by impulsiveness, hyperactivity, and difficulty paying attention. Kids with ADHD tend to struggle in school, make friends with difficulty, and injure themselves more frequently than their peers. A problem with ADHD is diagnosis is primarily subjective, with different thresholds of behavior used in different regions. For example, 7.3 percent of California children had received a diagnosis of ADHD in 2011, while during the same year, six states reported rates above 15 percent, according to the Centers for Disease Control and Prevention.

For the study, the researchers focused on the salience network, which is an interactive set of regions that work in synch when we decide where to direct our attention. Basically, this network assesses the importance of internal and external events and then focuses our thoughts and attention. While many things happen around us, only some grab our attention.

“The salience network helps us stop daydreaming or thinking about something that happened yesterday so we can focus on the task at hand,” explained Dr. Vinod Menon, senior author and a professor of psychiatry and behavioral sciences.

He and his co-researchers studied functional MRI brain scans from 180 children, half with ADHD and half without. The team scored each scan according to the synchronization between the salience network and two related neural networks: the default mode network, a set of regions directing self-referential activities such as daydreaming; and the central executive network, which manipulates information in working memory.

To focus our attention, Menon explained, the salience network turns down the activity of the default mode network and turns up the activity of the central executive network.

What Menon and his team discovered was the ADHD diagnosed children showed weaker interactions between these networks than children without the condition. In fact, the differences were large enough to distinguish the kids with ADHD from others. Separating out the children with ADHD, the researchers also identified worse scores on tests of inattentiveness linked to weaker interactions between the three brain networks.

This triple-network model “provides a novel, replicable, and parsimonious systems neuroscience framework” for diagnosing and predicting severity of childhood ADHD, the researchers concluded. 

Source: Cai W, Chen T, Szegletes L, Supekar K, Menon V. Aberrant Cross-Brain Network Interaction in Children With Attention-Deficit/Hyperactivity Disorder and Its Relation to Attention Deficits: A Multisite and Cross-Site Replication Study. Biological Psychiatry. 2015.

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