Those who don’t improve with intensive rehabilitation therapy, in contrast, fail to show early changes in brain activity, or they show changes that do not fit the normal pattern. Ultimately, ElMindA aims to use the device to determine more quickly whether a patient should switch treatments; though a larger clinical trial is needed to show whether this will significantly improve clinical outcomes.
Scientists have also used ElMindA’s system to characterize brain-activity patterns in patients with ADHD, identifying statistical parameters that differ between normal people and those with ADHD. Geva and collaborators aim to use the technology as a more objective way to diagnose the disorder.
“Many methods for diagnosing brain disorders, such as ADHD and depression, rely on pen-and-paper surveys, which is very removed from what we now know about the brain,” says Moshe Bar, a scientific advisor to ElMindA and a neuroscientist at the Martinos Center for Biomedical Imaging, which is affiliated with Harvard Medical School. “By being able to extract important parameters, we will be able to provide clinicians with something very unique.”
A larger clinical trial is about to begin at Harvard Medical School to test the effectiveness of the ElMindA system in diagnosing patients with ADHD and predicting which treatments are most effective. “Many children are getting Ritalin without any objective diagnosis,” says Geva. “And many adults don’t get Ritalin, even though they might be helped by it.”
Because the ElMindA system is portable, it’s more practical for daily use than magnetic resonance imaging, a brain imaging method that requires a huge magnet and can only be performed in medical centers. However, applying EEG-based techniques in a clinical setting can be tough, warns Guang Yue, a neuroscientist at the Cleveland Clinic in Ohio. Brain-activity patterns are highly variable, both between patients and within individuals. Scientists can compensate for this variability in research studies by asking patients to repeat the same task over and over and averaging the results. “In clinical settings, patients cannot do too many trials,” says Yue. “If you only do two or three trials, there’s a lot of noise and variation.”