Brain Patterns Can Inform Effective use of ADHD Medications

Although there are a wide variety of treatment options available for patients with attention deficit hyperactivity disorder (ADHD), methods for finding the proper course of treatment are still under development.

In a study published in Feedback, Jay Gunkleman examined the benefits of electroencephalography (EEG) and quantitative EEG (QEEG) readings to help healthcare professionals figure out the best way to treat their ADHD patients.

In his report, Gunkleman said doctors must first ensure the patient has been properly diagnosed with ADHD based on factors that include “impulsive, inattentive, and hyperactive behaviors across a period of time.” Then, the right method to treat the patient becomes the big question.

While medication is usually the first-line choice, Gunkleman said other treatment options like neurofeedback and neuromodulation are often overlooked. Presently, some of the most popular options include stimulants, amphetamine-related norepinephrine (NE) agonists, or “nonstimulant” NE-reuptake inhibitors, he continued.

“The medication intervention is hypothesized to operate through an increased engagement of the mirror neuron system, as reflected in the related EEG rhythm: Mu,” Gunkelman noted. “Mu is a normal EEG variant in the alpha frequency band, which can be seen in the EEG bicentrally in the absence of movement, intention to move, or even ‘engagement.’ ”

With so many available options, Gunkleman said it can be difficult for doctors to find the right treatment for a particular ADHD patient. Nevertheless, “the real trick is picking the right one the first time, or at least avoiding the obvious contraindications,” he noted.

During that long and tenuous process, Gunkleman said physicians might try to mix a variety of medications; however, that method increases the risk of side effects, which he said is “especially true if drugs are mismatched with the client’s underlying neurophysiological profile.”

In describing the risks of the “try one” method, Gunkleman cited statistics from the Star-D study that showed only a 38.6% initial trial efficacy for depression in a field of more than 3,000 patients. After a fourth set of trials, 33% of participants still complained of clinical depression, he said.

“Don’t dive into the water unless you know what is under the surface,” Gunkleman warned. “If clinical practitioners wish to ‘look’ before they just try one of this long list of medications, then they should look at the brain’s function prior to prescribing a medication to treat a client.”

Examining EEG results can be a key factor in anticipating how patients will respond to prescribed medications, Gunkleman noted. Potential observations can include excessive frontal theta, frontal slower frequency alpha, and frontal age-appropriate frequency alpha, in addition to beta spindles and paroxysmal or epileptiform discharges.

“All of these patterns can disturb the frontal lobe’s function, resulting in the same behavioral manifestation of the multiple physiological patterns, each representing a very different pathophysiology and predicting very different pharmacotheraputic approaches,” Gunkleman explained.

Gunkleman also demonstrated a “lock and key” system for matching proper medications with EEG readings. Among various situations, he suggested prescribing methylpheneidate for patients who have a frontal theta pattern, as well as those with slower-frequency alpha readings who need more NE released in their prescriptions.

However, when that method is not used or is unsuccessful, the author warned of rapid withdrawal symptoms in patients that could cause significant side effects such as dizziness, nausea, insomnia, anxiety, and even paresthesias. Depending on the type of medication, other possible side effects include stomach issues, mood instability, and sleep disturbances.