Using a unique fingerprint of brain activity to predict outcomes of TMS treatment for Major Depressive Disorder

Introduction

In the quest for more effective and personalised treatments for Major Depressive Disorder (MDD), researchers are exploring innovative ways to predict patient outcomes. Traditional approaches like medication and therapy have limitations in their one-size-fits-all approach. However, a recent study by Voetterl et al (2023) [1] has uncovered promising insights, focusing on the Individual Alpha Peak Frequency (IAF) measured through Electroencephalography (EEG) as a potential guide for tailoring treatments.

In this article, we will summarise the findings and implications of this new research, as well as outline issues with the current approach to psychiatric treatments and previous attempts to predict MDD treatment outcomes.

Issues with the Current Approach to Treatment

The current approach to psychiatric treatments for Major Depressive Disorder (MDD) and other conditions has several key flaws. Employing a one-size-fits-all strategy, practitioners prescribe medications based solely on patient symptoms, disregarding individual differences.

This uniform treatment model views MDD sufferers as a homogeneous group, assuming identical responses to treatment. Consequently, patients often endure a series of trial-and-error prescriptions, experimenting with multiple medications and dosages in the hope that one will have a positive effect. This approach can take weeks, months, and even years to bear fruit, during which time patients often must suffer through negative side effects such as weight-gain, sleep disturbances, and addiction to name a few [2].

Previous research has found that even with the potentially long and arduous trial-and-error approach, only 30% of patients using psychiatric medications achieve clinically significant improvements in their symptoms [3]. It is believed by many researchers that many of these issues could be addressed by considering the individual differences of patients.

For example, some researchers have found that a person’s unique genetic and molecular profiles can be used to predict their response to certain antidepressants [4]. With an estimated 300 million people affected by depressive disorders globally [5], individual differences are not only inevitable, but likely diverse and widespread. These issues highlight the need for a way to reliably predict treatment outcomes on a large scale.

Previous attempts to predict MDD treatment outcomes

Many previous attempts to predict treatment MDD outcomes have focused explicitly on medication. For example, a 2011 review of pharmacogenetics in antidepressant treatment identified several key genes associated with how individuals respond to these medications [4]. These genes include those related to how the body metabolises drugs. Variations in these genes can affect the rates at which antidepressants are processed, potentially influencing treatment outcomes. Additionally, genes related to neurotransmitter systems were found to modulate antidepressant response.

Another recent study explored the potential of using machine learning analysis of magnetic resonance imaging (MRI) data to predict the response of patients with major depressive disorder to several depression treatments [6]. The researchers conducted a systematic review and meta-analysis of 27 studies, involving 957 patients, that used MRI to predict individual responses to various antidepressant interventions, including medications and electroconvulsive therapy (ECT). The overall findings indicate that machine learning analysis of MRI data shows promise in predicting treatment outcomes for MDD, with an overall accuracy of 84%, sensitivity of 77%, and specificity of 79%.

A study from 2017 investigated whether the efficacy of repetitive transcranial magnetic stimulation (TMS) treatment for depression could be predicted using MRI [7]. The researchers found that individuals with depression had weaker connections in their frontostriatal circuits compared to healthy individuals. Notably, the study revealed that stronger connections between a specific frontal brain region (left dorsolateral prefrontal cortex or LDPFC) and the striatum predicted a better response to TMS treatment, suggesting that the strength of certain brain connections before starting TMS may help predict how well the treatment will work for individuals with depression.

Understanding IAF

In the present study by Voetterl et al (2023), the researchers attempted to build upon previous research into predicting treatment response to various MDD treatments [1]. However, where this study differs is through its method of prediction. While prior research focused on genetic biomarkers or MRI, Voetterl et al centred their study on IAF, or Individual Alpha Peak Frequency. IAF is a term in neuroscience and EEG that refers to a specific frequency within the alpha rhythm (typically between 7 and 13 Hertz) where an individual's brain waves peak. Think of it as a unique fingerprint for your brain activity. Essentially, IAF is like a personal brain signature that can tell us a lot about our cognitive abilities and mental well-being. By identifying and measuring these unique brain frequencies, researchers sought to predict responses to various treatments, such as antidepressants and brain stimulation therapies. The study aimed to move away from the conventional trial-and-error method towards a more targeted and personalised strategy.

Tailoring treatments with IAF

One key finding in this study was that people with slower IAF responded more favourably to antidepressant medications like sertraline. This unique correlation has significant implications for the field of psychiatric medications, as prescribers may soon be able to tailor a patient’s treatment to fit their own unique needs and therefore improve the likelihood of successful treatment.

Moreover, the study explored how IAF patterns could predict better responses to Repetitive Transcranial Magnetic Stimulation (rTMS). Researchers found that people with an IAF close to 10 Hz in frequency would respond better to rTMS at 10?Hz. This breakthrough could transform the landscape of depression treatment, providing a more efficient and effective path for those with certain brain wave patterns.

As a result of these ground-breaking findings, the researchers introduced a brain-based tool, called Brainmarker-I, to help doctors choose the best treatment for people with depression. This tool uses brain wave patterns measured by EEG to predict how well a person might respond to different treatments. The research tested this tool in people with depression, focusing on treatments like antidepressant medication, TMS, and ECT.

The results showed that using Brainmarker-I led to better outcomes, increasing the chances of improvement in depression.

Conclusion

The study by Voetterl et al (2023) represents a pivotal advance in the quest for more effective and personalised treatments for Major Depressive Disorder (MDD). Addressing the limitations of the current one-size-fits-all approach, the research focuses on the Individual Alpha Peak Frequency (IAF) measured through Electroencephalography (EEG) as a unique brain signature.

This innovative approach enables the prediction of individual responses to treatments such as antidepressants and brain stimulation therapies. Notably, the study's findings reveal correlations between slower IAF and favourable responses to specific medications, paving the way for tailored psychiatric treatments. The introduction of the Brainmarker-I tool, utilizing EEG-measured brain wave patterns, offers clinicians a means to predict treatment responses and signifies a transformative step towards more efficient and personalised interventions in the realm of Major Depressive Disorder.

References

[1] Voetterl, H.T.S., Sack, A.T., Olbrich, S.?et al.?Alpha peak frequency-based Brainmarker-I as a method to stratify to pharmacotherapy and brain stimulation treatments in depression.?Nat. Mental Health?1, 1023–1032 (2023). https://doi.org/10.1038/s44220-023-00160-7

[2] Van Westrhenen, R., & Ingelman-Sundberg, M. (2021). From trial and error to individualised pharmacogenomics-based pharmacotherapy in psychiatry.?Frontiers in Pharmacology,?12, 725565. https://doi.org/10.3389/fphar.2021.725565

[3] Walker, E., Kestler, L., Bollini, A., and Hochman, K. M. (2004). Schizophrenia: Etiology and Course.?Annu. Rev. Psychol.?55, 401-30. https://doi:10.1146/annurev.psych.55.090902.141950

[4] Porcelli S, Drago A, Fabbri C, Gibiino S, Calati R, Serretti A. Pharmacogenetics of antidepressant response. J Psychiatry Neurosci. 2011 Mar;36(2):87-113. doi: 10.1503/jpn.100059. PMID: 21172166; PMCID: PMC3044192.

[5] Rost, N., Binder, E.B. & Brückl, T.M. Predicting treatment outcome in depression: an introduction into current concepts and challenges.?Eur Arch Psychiatry Clin Neurosci?273, 113–127 (2023). https://doi.org/10.1007/s00406-022-01418-4

[6] Cohen, S.E., Zantvoord, J.B., Wezenberg, B.N.?et al.?Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis.?Transl Psychiatry?11, 168 (2021). https://doi.org/10.1038/s41398-021-01286-x

[7] Avissar, M., Powell, F., Ilieva, I., Respino, M., Gunning, F. M., Liston, C., & Dubin, M. J. (2017). Functional connectivity of the left DLPFC to striatum predicts treatment response of depression to TMS.?Brain stimulation,?10(5), 919-925. https://doi.org/10.1016/j.brs.2017.07.002