Milestone in personalised treatment for Depression
New study identifies Biomarkers in brain activity which predicts response to certain Depression treatments, a breakthrough in Psychiatry and use of EEG assessment technologies in mental healthcare.
Tests evaluating electrical activity in the brain can be an important tool in determining the correct treatment for Depression, according to a new study by the Stanford School of Medicine, in collaboration with the Brainclinics Foundation, Utrecht University and Maastricht University.
Many people with neuropsychiatric disorders (such as Depression or ADHD) try various medications or psychotherapy before they find adequate treatment. The results of the study recently published in Nature Biotechnology show a non-invasive quantitative electroencephalogram (QEEG) examination could help guide these people to the most effective treatment.
QEEGs are already a key assessment tool and feature of the neuroCare Group’s protocols for clinical assessment, allowing therapists to find more accurate and personalised Neurofeedback or Neurostimulation treatments. This new study suggests the QEEG labs used in neuroCare Clinics worldwide could become an important resource not only to determine suitable non-invasive neuromodulation therapies, but also for medication management in Psychiatry.
“Rather than relying on stepped care, these results suggest patients will get the right treatment more quickly, and we can skip unnecessary steps of treatment,” says researcher Dr Martijn Arns of Utrecht University, study co-author and neuroCare advisor.
At neuroCare Clinics, clients are given personalised therapy recommendations based on a detailed QEEG assessment conducted in clinic.
From one-size-fits-all to “precision psychiatry”
People with Depression are commonly prescribed antidepressant medications, but these are only modestly more effective than placebo medications. The authors of the study say this is partly because the clinical diagnosis of major Depression encompasses biologically heterogeneous conditions. That is, there are various biological causes for depressive symptoms.
This large-scale study is part of the development of “precision psychiatry”, which Arns says aims to move beyond a “one-size-fits-all” approach to treating neuropsychiatric disorders.
“This outcome is an important step towards stratified medicine, in which treatment choice is informed by specific characteristics of subgroups, so-called biomarkers,” according to Arns.
A “Biomarker” is the term for a biological marker or feature in an individual which can determine if they will or won’t respond to a treatment. In this case a biomarker was identified to predict response to the antidepressant medication Sertraline. The researchers found that the absence of this same biomarker was associated with better response to non-invasive Transcranial Magnetic Stimulation (TMS).
“This biomarker offers possibilities for more stratified psychiatry within the class of antidepressant treatments”, says Arns.
Machine learning of brain activity
The researchers at Stanford School of Medicine deployed machine learning to identify biomarkers in brain activity that provide clues for which treatment would be most effective.
They developed an algorithm that recognises a biomarker in EEGs predicting the effect of the antidepressant medication Sertraline. The same machine-learning algorithm was then also applied to an EEG dataset of nearly two hundred patients who had been treated with non-pharmaceutical rTMS therapy.
The results showed that the absence of the sertraline predictor indicates that someone will respond better to a specific rTMS protocol, a stimulation therapy proven effective for severe Depression.
“How beautiful is it when we know before treatment whether medication makes sense, or whether it is better to apply another treatment such as psychotherapy or rTMS?” says Arns.
Personalised treatment on the horizon
For people with neuropsychiatric disorders, getting access to personalised treatment is one step closer. Arns says: “This technology (EEG) is already very accessible and used in many clinics including neuroCare, and we expect EEG-based stratification to become available in 12-18 months from now”.
Tom Mechtersheimer, neuroCare Founder and Executive Chairman has made the following comments: “This is a very good example of how we keep pushing the mental health and performance field towards personalised and evidence-based world-best clinical outcomes".
neuroCare uses QEEG assessments to examine brain activity for explanations of negative symptoms and feelings. This allows neuroCare therapists to design a more targeted and personalised therapy program for patients. This study’s findings show the QEEG diagnostic tool could help a Psychiatrist determine which treatment would be right for their patients.
“The neuroCare approach is not only more focal and causal in its working pathway but also we help clinicians with our Standard Operating Procedures to first better understand and assess and then rewire the human patterns of feeling and behaving in a more personalised way,” says Mechtersheimer. “This is the core advantage compared to the common “one size fits all medication only” approach. It should be noted, that we also integrate medication in our holistic approach where helpful for a shorter term or acute intervention, but our focus is on the sustainable rewiring of a human’s behavioural patterns which is much more productive and economical for patients, clinicians, payors and society at large.”
To learn more about QEEG assessments at neuroCare Clinics worldwide visit:
neuroCare Clinics Australia
neuroCare Clinics Italy
neuroCare Clinics Netherlands
neuroCare Centers of America
For Researchers and Professionals:
Wu, W., Zhang, Y., Jiang, J., Lucas, M., Fonzo, G., Rolle, C., Cooper, C., Chin-Fatt, C., Krepel, N., Cornelssen, C., Wright, R., Toll, R., Trivedi, H., Monuszko, K., Caudle, T., Sarhadi, K., Jha, M., Trombello, J., Deckersbach, T., Adams, P., McGrath, P., Weissman, M., Fava, M., Pizzagalli, D., Arns, M., Trivedi, M., Etkin, A. (2020). An electroencephalographic signature predicts antidepressant response in major depression Nature Biotechnology https://dx.doi.org/10.1038/s41587-019-0397-3