From subjective to objective

After the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), which is known to be problematic for the purposes of diagnosing depression due to unclear criteria and subjective measurements [1,2], the National Institute of Mental Health (NIMH) started their work on Research Domain Criteria (RDoC) [3], a new approach to studying mental disorders.

According to RDoC, mental disorders should be studied in the context of six different domains, such as cognitive systems and negative valence systems. As they state, “the goal is to understand the nature of mental health and illness in terms of varying degrees of dysfunction in general psychological/biological systems” [4]. In our project, we implement an RDoC approach to study depression.

RDoC domains


Negative Valance


Positive Valence



Social Process

During our research and data collection, we use different measurements for each domain, including DNA analysis, psychiatric assessments, laboratory tests, and most importantly, electroencephalography (EEG), which we plan to collect not only during rest state but also during task performance to measure activities of different brain circuits.

Like NIMH and other researchers in the fields of psychiatry and neuroscience [4,5], we believe that depression is defined by a combination of symptoms, which may be the result of different factors. In other words, a depression diagnosis includes several sub-diagnoses of different diseases that require a specific treatment approach. Indeed, Drysdale et al. [4], after analyzing fMRI data on resting-state brain connectivity, identified at least four different biomarkers and thus, four clusters of patients initially diagnosed with depression.


In order to determine the proper treatment for patients, it is necessary to have an instrument that will help to understand the neural and biological factors underlying the symptoms of depression. Providing this instrument is the goal of our company. However, we understand that we cannot simply use Drysdale et al.’s 

approach, because fMRIs are not affordable for most patients and require a considerable amount of time to perform. Therefore, in our project, we plan to rely mostly on EEG, which can show the activity of all neural circuits involved in depression [6,7] and is a well-known, reliable, and affordable instrument for clinicians. 


EEG Utility in Diagnosis and Monitoring Treatment Outcomes

According to numerous studies [7,8,9,10,11,12], EEG data are also promising for the identification of depression biomarkers and for treatment prediction. Our advisors, Brainclinics, have already demonstrated that treatment based on EEG biomarkers has a significantly better outcome than treatment-as-usual (TAU) [13]. Importantly, these promising results were achieved with simple EEG data analysis without the application of more complex modern data-science approaches. Our company plans to integrate the knowledge of EEG and psychiatry from leading neuroscientists and the potential of data analysis provided by machine learning.


The application of machine learning to study EEG signals has already shown promising results. For example, researchers from the alphabet’s experimental projects group, X (a Google sister company), spent three years developing machine-learning models to analyze EEG data [14]. In this project (Amber), researchers demonstrated significant results in the identification of anhedonia, or the inability to feel pleasure, which is one of the main domains of depression research. However, their main goal was not just to learn how to identify and measure depression but rather to do so quickly and easily using a simple tool with only a few channels. Our company, in contrast, wants to apply their mathematical experience but to standardized EEG data with signals from 33 channels. 

Combine expertise from different domains



Data science

Referenses learn more

[1] Koukopoulos, A., Sani, G., & Ghaemi, S. (2013). Mixed features of depression: Why DSM-5 is wrong (and so was DSM-IV). British Journal of Psychiatry, 203(1), 3–5. doi:10.1192/bjp.bp.112.124404
[2] Wakefield, J. C., & First, M. B. (2012). Validity of the bereavement exclusion to major depression: Does the empirical evidence support the proposal to eliminate the exclusion in DSM-5? World Psychiatry, 11(1), 3–10. 
[3] National Institute of Mental Health. (2021, July 15). Research Domain Criteria (RDoC).
[4] Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., & Schatzberg, A. F. (2017). Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine, 23(1), 28–38. 
[5] Arns, M., Meijs, H., Lin, B., van Wingen, G., Gordon, E., Denys, D., De Wilde, B., Van Hecke, J., Niemegeers, P., van Eijk, K., & Luykx, J. (2021). Can polygenic-informed EEG biomarkers predict differential antidepressant treatment response? An EEG stratification marker for rTMS and sertraline. 
[6] Gerrits, B., Vollebregt, M. A., Olbrich, S., van Dijk, H., Palmer, D., Gordon, E., Pascual-Marqui, R., Kessels, R. P., & Arns, M. (2019). Probing the “Default Network Interference Hypothesis” with EEG: An RDoC approach focused on attention. Clinical EEG and Neuroscience, 50(6), 404–412. 
[7] Keren, H., O’Callaghan, G., Vidal-Ribas, P., Buzzell, G. A., Brotman, M. A., Leibenluft, E., Kaiser, A., Wolke, S., & Pine, D. S. (2018). Reward processing in depression: A conceptual and meta-analytic review across fMRI and EEG studies. American Journal of Psychiatry, 175(11), 1111–1120. 
[8] Williams, L. M., Rush, A. J., Koslow, S. H., Wisniewski, S. R., Cooper, N. J., Nemeroff, C. B., Schatzberg, A. F., & Gordon, E. (2011). International study to predict optimized treatment for depression (iSPOT-D), a randomized clinical trial: Rationale and protocol. Trials, 12(1), 1–17. 
[9] Whitton, A. E., Deccy, S., Ironside, M. L., Kumar, P., Beltzer, M., & Pizzagalli, D. A. (2018). Electroencephalography source functional connectivity reveals abnormal high-frequency communication among large-scale functional networks in depression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(1), 50–58. 
[10] Shahabi, M. S., & Maghsoudi, A. (2021). Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG. Biocybernetics and Biomedical Engineering. 
[11] Wu, W., Zhang, Y., Jiang, J., Lucas, M. V., Fonzo, G. A., Rolle, C. E., Cooper, C., Chin-Fatt, C., Krepel, N., Cornelssen, C. A., Wright, R., & Etkin, A. (2020). An electroencephalographic signature predicts antidepressant response in major depression. Nature Biotechnology, 38(4), 439–447. 
[12] Schiller, M. J. (2019). Quantitative electroencephalography in guiding treatment of major depression. Frontiers in Psychiatry, 9, 779. 
[13] van der Vinne, N., Vollebregt, M. A., Rush, A. J., Eebes, M., van Putten, M. J., & Arns, M. (2021). EEG biomarker informed prescription of antidepressants in MDD: A feasibility trial. European Neuropsychopharmacology, 44, 14–22. 

[14] Honke, G., Higgins, I., Thigpen, N., Miskovic, V., Link, K., Duan, S., Gupta, P., Klawohn, J., & Hajcak, G. (2020). Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. arXiv preprint arXiv:2010.15274.