Protein pairing builds brain networks

Neural networks are formed by the interconnection of specific neurons in the brain. The molecular mechanisms involved in creating these connections, however, have so far eluded scientists. Research led by Jun Aruga from the RIKEN Brain Science Institute has now identified an interaction between two proteins that is crucial for making connections between specific types of neurons, with implications for some neurological disorders.

Connections between neurons are made via synapses—small gaps across which chemicals called neurotransmitters pass, relaying signals from a presynaptic neuron to a postsynaptic neuron. Aruga and his colleagues focused on a protein called mGluR7, which is found only at synapses with a specific type of postsynaptic neuron in an area of the brain involved in forming memories.

“mGluR7 is located on the presynaptic side of connections made with hippocampal local inhibitory neurons,” explains Aruga. “Previous studies have proposed that this protein prevents neurotransmitter release from the presynaptic neuron when the neurotransmitter glutamate binds to it.”

The researchers discovered that the localization of mGluR7 to specific synapses is determined by the presence of another protein called Elfn1. This protein is found on the other side of the same synapses, directly opposite mGluR7. When the researchers artificially introduced Elfn1 into cultured cells, mGluR7 became associated with the same cells, and they showed that this was due to a physical interaction between the two proteins. Conversely, deleting Elfn1 in the brains of mice reduced the amount of mGluR7 at the synapses.

These changes interfered with the process of strengthening connections at synapses, which takes place during memory formation, and caused patterns of brain waves that indicated abnormally high levels of electrical activity. Genetically altered mice also exhibited other symptoms that resembled human conditions.

“Deleting Elfn1 increased the susceptibility of mice to seizures,” explains Aruga. “It also enhanced behaviors similar to attention deficit hyperactivity disorder (ADHD).”

Indeed, the researchers found that humans with epilepsy and ADHD also had a faulty version of the gene encoding Elfn1, suggesting that a deficit in the ability of Elfn1 to localize mGluR7 and form specific connections in neural networks is important in some neurological conditions.

“In combination, the human and mouse results implicate the Elfn1–mGluR7 complex in the pathophysiology of epilepsy and ADHD, at least in part,” explains Aruga, although he remains cautious at this early stage of research. “Because of sample size limitations, the human genetics result is not conclusive, but we are now awaiting the results of follow-up studies with additional subjects.”

(Image caption: These figures show lagged maturation of connections in ADHD between the default mode network, involved in internally-directed thought (i.e., daydreaming) and shown on the left of each figure, and two brain networks involved in externally-focused attention, shown on the right of each figure. The width of each arc represents the number of lagged connections between two regions within each network. Connections that normally increase with age and that are hypoconnected in ADHD are shown in blue; connections that normally decrease with age and that are hyperconnected in ADHD are shown in red.)

Slow to mature, quick to distract: ADHD brain study finds slower development of key connections

A peek inside the brains of more than 750 children and teens reveals a key difference in brain architecture between those with attention deficit hyperactivity disorder and those without.

Kids and teens with ADHD, a new study finds, lag behind others of the same age in how quickly their brains form connections within, and between, key brain networks.

The result: less-mature connections between a brain network that controls internally-directed thought (such as daydreaming) and networks that allow a person to focus on externally-directed tasks. That lag in connection development may help explain why people with ADHD get easily distracted or struggle to stay focused.

What’s more, the new findings, and the methods used to make them, may one day allow doctors to use brain scans to diagnose ADHD — and track how well someone responds to treatment. This kind of neuroimaging “biomarker” doesn’t yet exist for ADHD, or any psychiatric condition for that matter.

The new findings come from a team in the University of Michigan Medical School’s Department of Psychiatry. They used highly advanced computing techniques to analyze a large pool of detailed brain scans that were publicly shared for scientists to study. Their results are published in the Proceedings of the National Academy of Sciences.

Lead author Chandra Sripada, M.D., Ph.D., and colleagues looked at the brain scans of 275 kids and teens with ADHD, and 481 others without it, using “connectomic” methods that can map interconnectivity between networks in the brain.

The scans, made using function magnetic resonance imaging (fMRI) scanners, show brain activity during a resting state. This allows researchers to see how a number of different brain networks, each specialized for certain types of functions, were “talking” within and amongst themselves.

The researchers found lags in development of connection within the internally-focused network, called the default mode network or DMN, and in development of connections between DMN and two networks that process externally-focused tasks, often called task-positive networks, or TPNs. They could even see that the lags in connection development with the two task-related networks — the frontoparietal and ventral attention networks — were located primarily in two specific areas of the brain.

The new findings mesh well with what other researchers have found by examining the physical structure of the brains of people with and without ADHD in other ways.

Such research has already shown alterations in regions within DMN and TPNs. So, the new findings build on that understanding and add to it. 

The findings are also relevant to thinking about the longitudinal course of ADHD from childhood to adulthood. For instance, some children and teens “grow out” of the disorder, while for others the disorder persists throughout adulthood. Future studies of brain network maturation in ADHD could shed light into the neural basis for this difference.

“We and others are interested in understanding the neural mechanisms of ADHD in hopes that we can contribute to better diagnosis and treatment,” says Sripada, an assistant professor and psychiatrist who holds a joint appointment in the U-M Philosophy department and is a member of the U-M Center for Computational Medicine and Bioinformatics. “But without the database of fMRI images, and the spirit of collaboration that allowed them to be compiled and shared, we would never have reached this point.”  

Sripada explains that in the last decade, functional medical imaging has revealed that the human brain is functionally organized into large-scale connectivity networks. These networks, and the connections between them, mature throughout early childhood all the way to young adulthood. “It is particularly noteworthy that the networks we found to have lagging maturation in ADHD are linked to the very behaviors that are the symptoms of ADHD,” he says. 

Studying the vast array of connections in the brain, a field called connectomics, requires scientists to be able to parse through not just the one-to-one communications between two specific brain regions, but the patterns of communication among thousands of nodes within the brain. This requires major computing power and access to massive amounts of data – which makes the open sharing of fMRI images so important.

“The results of this study set the stage for the next phase of this research, which is to examine individual components of the networks that have the maturational lag,” he says. “This study provides a coarse-grained understanding, and now we want to examine this phenomenon in a more fine-grained way that might lead us to a true biological marker, or neuromarker, for ADHD.”

Sripada also notes that connectomics could be used to examine other disorders with roots in brain connectivity – including autism, which some evidence has suggested stems from over-maturation of some brain networks, and schizophrenia, which may arise from abnormal connections. Pooling more fMRI data from people with these conditions, and depression, anxiety, bipolar disorder and more could boost connectomics studies in those fields.

ADHD children make poor decisions due to less differentiated learning processes

Which shirt do we put on in the morning? Do we drive to work or take the train? From which takeaway joint do we want to buy lunch? We make hundreds of different decisions every day. Even if these often only have a minimal impact, it is extremely important for our long-term personal development to make decisions that are as optimal as possible. People with ADHD often find this difficult, however. They are known to make impulsive decisions, often choosing options which bring a prompt but smaller reward instead of making a choice that yields a greater reward later on down the line. Researchers from the University Clinics for Child and Adolescent Psychiatry, University of Zurich, now reveal that different decision-making processes are responsible for such suboptimal choices and that these take place in the middle of the frontal lobe.

Mathematical models help to understand the decision-making processes

In the study, the decision-making processes in 40 young people with and without ADHD were examined. Lying in a functional magnetic resonance imaging scanner to record the brain activity, the participants played a game where they had to learn which of two images carried more frequent rewards. In order to understand the impaired mechanisms of participants with ADHD better, learning algorithms which originally stemmed from the field of artificial intelligence were used to evaluate the data. These mathematical models help to understand the precise learning and decision-making mechanisms better. “We were able to demonstrate that young people with ADHD do not inherently have difficulties in learning new information; instead, they evidently use less differentiated learning patterns, which is presumably why sub-optimal decisions are often made”, says first author Tobias Hauser.

Multimodal imaging affords glimpses inside the brain

In order to study the brain processes that triggered these impairments, the authors used multimodal imaging methods, where the participants were examined using a combined measurement of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to record the electrical activity and the blood flow in the brain. It became apparent that participants with ADHD exhibit an altered functioning in the medial prefrontal cortex – a region in the middle of the frontal lobe. This part of the brain is heavily involved in decision-making processes, especially if you have to choose between several options, and in learning from errors. Although a change in activity in this region was already discovered in other contexts for ADHD, the Zurich researchers were now also able to pinpoint the precise moment of this impairment, which already occurred less than half a second after a feedback, i.e. at a very early stage.

Psychologist Tobias Hauser, who is now researching at the Wellcome Trust Centre for Neuroimaging, University College London, is convinced that the results fundamentally improve our understanding of the mechanisms of impaired decision-making behavior in people with ADHD. The next step will be to study the brain messenger substances. “If our findings are confirmed, they will provide key clues as to how we might be able to design therapeutic interventions in future,” explains Hauser.

Literature:

Tobias U. Hauser, Reto Iannaccone, Juliane Ball, Christoph Mathys, Daniel Brandeis, Susanne Walitza & Silvia Brem: Role of Medial Prefrontal Cortex in Impaired Decision Making in Juvenile Attention-Deficit/Hyperactivity Disorder, in: JAMA Psychiatry

MRI Technique May Help Prevent ADHD Misdiagnosis

Brain iron levels offer a potential biomarker in the diagnosis of attention deficit hyperactivity disorder (ADHD) and may help physicians and parents make better informed treatment decisions, according to new research published online in the journal Radiology.

ADHD is a common disorder in children and adolescents that can continue into adulthood. Symptoms include hyperactivity and difficulty staying focused, paying attention and controlling behavior. The American Psychiatric Association reports that ADHD affects 3 to 7 percent of school-age children.

Psychostimulant medications such as Ritalin are among the drugs commonly used to reduce ADHD symptoms. Psychostimulants affect levels of dopamine, a neurotransmitter in the brain associated with addiction.

"Much debate and concern has emerged regarding the continual rise of ADHD diagnosis in the U.S. given that two-thirds of those diagnosed receive psychostimulant medications," said Vitria Adisetiyo, Ph.D., postdoctoral research fellow at the Medical University of South Carolina in Charleston, S.C. "We wanted to see if we could identify brain iron as a potential noninvasive biomarker for medication-naïve ADHD to prevent misdiagnosis."

For the study, the research team measured brain iron levels in 22 children and adolescents with ADHD, 12 of whom had never been on medication for their condition (medication naïve), and 27 healthy control children and adolescents using a magnetic resonance imaging (MRI) technique called magnetic field correlation imaging. The technique was introduced in 2006 by study co-authors and faculty members Joseph A. Helpern, Ph.D., and Jens H. Jensen, Ph.D. No contrast agents were used, and blood iron levels in the body were measured using a blood draw.

The results showed that the 12 ADHD medication-naïve patients had significantly lower brain iron levels than the 10 ADHD patients who had been on psychostimulant medication and the 27 children and adolescents in the control group. In contrast, ADHD patients with a history of psychostimulant medication treatment had brain iron levels comparable to controls, suggesting that brain iron may increase to normal levels with psychostimulant treatment.

"Our research suggests that iron absorption into the brain may be abnormal in ADHD given that atypical brain iron levels are found even when blood iron levels in the body are normal," Dr. Adisetiyo said. "We found no differences in blood iron measures between controls, medication-naïve ADHD patients or pscyhostimulant-medicated ADHD patients."

Magnetic field correlation imaging’s ability to noninvasively detect the low iron levels may help improve ADHD diagnosis and guide optimal treatment. Currently, ADHD diagnosis is based only on subjective clinical interviews and questionnaires. Having a biological biomarker may help inform clinical diagnosis, particularly in borderline cases, Dr. Adisetiyo noted.

If the results can be replicated in larger studies, magnetic field correlation might have a future role in determining which patients would benefit from psychostimulants—an important consideration because the drugs can become addictive if taken inappropriately and lead to abuse of other drugs like cocaine.

"We want the public to know that progress is being made in identifying potential noninvasive biological biomarkers of ADHD which may help to prevent misdiagnosis," Dr. Adisetiyo said. "We are currently testing our findings in a larger cohort to confirm that measuring brain iron levels in ADHD is indeed a reliable and clinically feasible biomarker."

(Image caption: At left, the brains of adults who had ADHD as children but no longer have it show synchronous activity between the posterior cingulate cortex (the larger red region) and the medial prefrontal cortex (smaller red region). At right, the brains of adults who continue to experience ADHD do not show this synchronous activity. Illustration: Jose-Luis Olivares/MIT, based on images courtesy of the researchers)

Inside the adult ADHD brain

About 11 percent of school-age children in the United States have been diagnosed with attention deficit hyperactivity disorder (ADHD). While many of these children eventually “outgrow” the disorder, some carry their difficulties into adulthood: About 10 million American adults are currently diagnosed with ADHD.

In the first study to compare patterns of brain activity in adults who recovered from childhood ADHD and those who did not, MIT neuroscientists have discovered key differences in a brain communication network that is active when the brain is at wakeful rest and not focused on a particular task. The findings offer evidence of a biological basis for adult ADHD and should help to validate the criteria used to diagnose the disorder, according to the researchers.

Diagnoses of adult ADHD have risen dramatically in the past several years, with symptoms similar to those of childhood ADHD: a general inability to focus, reflected in difficulty completing tasks, listening to instructions, or remembering details.

“The psychiatric guidelines for whether a person’s ADHD is persistent or remitted are based on lots of clinical studies and impressions. This new study suggests that there is a real biological boundary between those two sets of patients,” says MIT’s John Gabrieli, the Grover M. Hermann Professor of Health Sciences and Technology, professor of brain and cognitive sciences, and an author of the study, which appears in the June 10 issue of the journal Brain.

Shifting brain patterns

This study focused on 35 adults who were diagnosed with ADHD as children; 13 of them still have the disorder, while the rest have recovered. “This sample really gave us a unique opportunity to ask questions about whether or not the brain basis of ADHD is similar in the remitted-ADHD and persistent-ADHD cohorts,” says Aaron Mattfeld, a postdoc at MIT’s McGovern Institute for Brain Research and the paper’s lead author.

The researchers used a technique called resting-state functional magnetic resonance imaging (fMRI) to study what the brain is doing when a person is not engaged in any particular activity. These patterns reveal which parts of the brain communicate with each other during this type of wakeful rest.

“It’s a different way of using functional brain imaging to investigate brain networks,” says Susan Whitfield-Gabrieli, a research scientist at the McGovern Institute and the senior author of the paper. “Here we have subjects just lying in the scanner. This method reveals the intrinsic functional architecture of the human brain without invoking any specific task.”

In people without ADHD, when the mind is unfocused, there is a distinctive synchrony of activity in brain regions known as the default mode network. Previous studies have shown that in children and adults with ADHD, two major hubs of this network — the posterior cingulate cortex and the medial prefrontal cortex — no longer synchronize.

In the new study, the MIT team showed for the first time that in adults who had been diagnosed with ADHD as children but no longer have it, this normal synchrony pattern is restored. “Their brains now look like those of people who never had ADHD,” Mattfeld says.

“This finding is quite intriguing,” says Francisco Xavier Castellanos, a professor of child and adolescent psychiatry at New York University who was not involved in the research. “If it can be confirmed, this pattern could become a target for potential modification to help patients learn to compensate for the disorder without changing their genetic makeup.”

Lingering problems

However, in another measure of brain synchrony, the researchers found much more similarity between both groups of ADHD patients.

In people without ADHD, when the default mode network is active, another network, called the task positive network, is suppressed. When the brain is performing tasks that require focus, the task positive network takes over and suppresses the default mode network. If this reciprocal relationship degrades, the ability to focus declines.

Both groups of adult ADHD patients, including those who had recovered, showed patterns of simultaneous activation of both networks. This is thought to be a sign of impairment in executive function — the management of cognitive tasks — that is separate from ADHD, but occurs in about half of ADHD patients. All of the ADHD patients in this study performed poorly on tests of executive function. “Once you have executive function problems, they seem to hang in there,” says Gabrieli, who is a member of the McGovern Institute.

The researchers now plan to investigate how ADHD medications influence the brain’s default mode network, in hopes that this might allow them to predict which drugs will work best for individual patients. Currently, about 60 percent of patients respond well to the first drug they receive.

“It’s unknown what’s different about the other 40 percent or so who don’t respond very much,” Gabrieli says. “We’re pretty excited about the possibility that some brain measurement would tell us which child or adult is most likely to benefit from a treatment.”

Training the Brain to Focus

About one in 10 school children suffers from attention deficit/hyperactivity disorder (ADHD), according to the Centers for Disease Control and Prevention. Linked to measurable differences in children’s brain structures and brain waves, ADHD can have dire effects on children’s academic achievements and lead to disrupted classrooms.

The CDC reports that as many as 3 million American elementary school children now take medications to control their symptoms. But these drugs don’t work for everyone. Worse, their potential side effects can have serious consequences for kids who also have heart conditions, eating or digestive problems or mood disorders such as depression.

In a recent study, Naomi J. Steiner, director of the CATS Project (Computer Attention Training in Schools for children with ADHD) at Tufts Medical Center, and her colleagues found that computer-based attention-training exercises significantly improved the ability of kids with ADHD to focus and pay attention.

The team tested two kinds of computer training systems. The first, computer cognitive attention training, uses computerized brain exercises to strengthen key mental skills such as short-term memory, eye-hand coordination and visual processing through a series of game-like activities. The second, neurofeedback, measures children’s brain waves in real time and provides visual and auditory feedback that can help them harness their ability to focus. The researchers found that both systems ameliorated the symptoms of ADHD, with neurofeedback outperforming computer cognitive attention training.

What’s more, the team found that the effect lasted months after the computer-based training sessions ended. The results of the large-scale clinical trial, published earlier this year in the journal Pediatrics, bolster the positive findings Steiner and her colleagues saw in a pilot study they conducted previously.

That’s encouraging news, because these therapies—some of which are commercially available to the public and many of which have been adopted by school systems in every state—aren’t yet covered by health insurance policies, nor will they be without a data showing their efficacy. Steiner’s body of research is one more step down that road. (See the story “Your Brain on Video Games.”)

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