From ‘one-size-fits-all psychiatry’ to ‘precision psychiatry’ in ADHD and Depression.
Recently the landscape in psychiatry is undergoing a dramatic change. Some large-scale studies investigating the effects of conventional treatments for ADHD and depression in practice have demonstrated on the group-level limited efficacy of antidepressant medication and cognitive behavioral therapy in depression (STAR*D: Rush et al., 2006), an overestimation of the effects of cognitive-behavioural therapy for depression as a result of publication bias (Cuijpers, Smit, Bohlmeijer, Hollon, & Andersson, 2010) and limited long-term effects of stimulant medication, multicomponent behavior therapy and multimodal treatment in ADHD (NIMH-MTA trial: Molina et al., 2009). Furthermore, several large pharmaceutical companies have announced to ‘…pull the plug on drug discovery in some areas of neuroscience….’ (Miller, 2010) including GlaxoSmithKline (GSK) and AstraZeneca. Figure 1 further demonstrates this trend of declining publication rates of pharmacological treatments in ADHD and Depression. On one hand, this can be considered a worrying development, since there is still much to improve in treatments for depression and ADHD. On the other hand, it also reflects a problem widely present in psychiatry: that neurobiology does not map very well on behavior and psychology. A drug called an ‘antidepressant’ on average only benefits approximately 40% of patients with a Depression, thereby suggesting substantial neurobiological heterogeneity. Therefore, a move beyond data regarding the average effectiveness of treatment, to identify the best treatment for any individual (Simon & Perlis, 2010) or personalized medicine is crucial. In personalized medicine it is the goal to prescribe the right treatment, for the right person at the right time as opposed to the current ‘trial-and-error’ approach, by using biomarkers of endophenotypes.
In addition to this development we also witness a shift from a ‘systemic treatment approach’ (i.e. systemically applying medication to the whole body) to a more ‘focal treatment approach’ also subsumed under the term ‘Neuromodulation’. In this development there are currently many new treatments developed and applied such as deep-brain stimulation in depression (Hamani et al., 2011); Parkinson: (Zahodne et al., 2009), rTMS in depression (Schutter 2010; Schutter 2009a), fMRI neurofeedback in pain (deCharms et al., 2005), neurofeedback in ADHD (Arns, de Ridder, Strehl, Breteler & Coenen, 2009), Vagus Nerve Stimulation (VNS) in depression (Daban, Martinez-Aran, Cruz & Vieta, 2008) etc. See figure 1 below, where – in contrast to ‘drug-treatments’ – the number of scientific publications demonstrates a strong growth over the years for neuromodulation techniques such as rTMS and Neurofeedback.
Figure 1: This figure demonstrates the number of scientific publications over the years, covering ‘drug-treatments’ in Depression and ADHD (left) and the number of publications for non-pharmacological neuromodulation treatments such as neurofeedback in ADHD and rTMS in depression (right). This figure further confirms the trend that the pharmaceutical industry has suspended most of it’s R&D budgets in CNS drugs, reflected as declining publication rates, whereas there is an exponential growth in scientific publications on non-pharmacological treatments such as neurofeedback and rTMS (data from Scopus).
Along with the development of these new techniques it is interesting to note that the application of some of these neuromodulation approaches do not solely rely on a DSM-5 diagnosis, but lean more towards identifying dysfunctional brain networks and application of treatment to specifically modulate those networks. For example, deep brain stimulation studies specifically aim to modulate the subcallosal cingulate gyrus (Hamani et al., 2011), fMRI neurofeedback patients learn to specifically regulate activity in the rostral anterior cingulate (deCharms et al., 2005) and for neurofeedback treatment in ADHD, a standard neurofeedback protocol is stratified based on the individual EEG (Arns et al. 2012).
As pointed out above, a focus on biomarkers and endophenotypes which can predict treatment outcome will become crucial to improving treatments for ADHD and depression. The development of personalized medicine also referred to as precision psychiatry, is hence a very important development in psychiatry. At the Brainclinics Foundation we have for the last 20 years focused on this development of personalized psychiatry. To this end much of our research has focussed on finding reliable predictors for response and non-response to various treatments in ADHD and depression, as can be read an article that we collaborated on, in Nature Biotechnology, recently.
While our main aim has been precision psychiatry, currently considered the holy grail in psychiatry and medicine, such a development where treatments are truly individualized to the individual patient seems rather complex. The main issue with this approach is that it is difficult to demonstrate efficacy for treatments applied on this individual level, actually constituting many N=1 studies. Based on recent studies, we have come across several biomarkers that were either ‘drug-class’ specific biomarker (Arns et al. 2015) and drug-specific biomarkers (Arns, Gordon & Boutros, 2017). Knowing that for example most antidepressant medications have equal clinical efficacy on the group level as demonstrated in randomized studies such as iSPOT-D, treatment stratification to an antidepressant based on a biomarker is unlikely to do harm, but migth resolve and (partly) address the inherent neurobiological heterogeneity thus resulting in higher efficacy relative to a one-size-fits-all psychiatry approach. These three approaches are summarised in figure 2 below.
Figure 2: An infographic summarizing the current ‘one-size-fits-all psychiatry’ that is currently in use. The holy grail in psychiatry is ‘precision psychiatry’ or personalized medicine, where, using biomarkers, individual patients are prescribed to the right individual treatment at the right time. However, ‘stratified psychiatry’ is likely an intermediate step before achieving precision psychiatry in depression and ADHD. Here, multiple effective treatments are identified that have already demonstrated eficacy in for example the treatment of depression, such as antidepressants, rTMS, ECT, DBS, and biomarkers are used to stratify patients within a DSM-5 diagnosis to a choice of known and effective treatments, thereby achieving better clinical outcomes.
Below also find an illustration of the current ‘one-size-fits-all psychiatry’ approach with a wink 😉
Wu, A., Zhang, Y., Jiang, J., Luca, M.V., Fonzo, G.A., Rolle, C.E., Cooper, C., Chin-Fatt, C., Krepel, N., Cornelssen, C.A., Wright, R., Toll, R.T, Trivedi, H.M., Monuszko, K., Caudle, T.L., Sarhadi, K., Jha, M.K., Trombello, J.M., Deckersbach, T., Adams, P., McGrath, P.J., Weissman, M.M., Fava, M., Pizzagalli, D.A., Arns, M., Madhukar H. Trivedi, M.H., Etkin, A., An electroencephalographic signature predicts antidepressant response in major depression. Nature Biotechnology https://doi.org/10.1038/s41587-019-0397-3
van der Vinne,, N., Vollebregt, M., van Putten, M.J.A.M., Arns, M. (2019). Stability of frontal alpha asymmetry in depressed patients during antidepressant treatment. NeuroImage. Clinical 24(), 102056. https://dx.doi.org/10.1016/j.nicl.2019.102056
van der Vinne, N., Vollebregt, M.A., Boutros, N.N., Fallahpour, K., van Putten, M.J.A.M., Arns, M. (2019) Normalization of EEG in depression after antidepressant treatment with sertraline? A preliminary report. Journal Of Affective Disorders, 259 (2019) 67-72 doi: 10.1016/j.jad.2019.08.016
Krepel, N., Rush, A. J., Iseger, T. A., Sack, A. T., & Arns, M. (2019). Can psychological features predict antidepressant response to rtms? A discovery-replication approach. Psychological Medicine. doi:https://doi.org/10.1017/S0033291718004191
Benschop, L., Baeken, C., Vanderhasselt, M., Van de Steen, F., Van Heeringen, K., Arns, M. (2019). Electroencephalogram Resting State Frequency Power Characteristics of Suicidal Behavior in Female Patients With Major Depressive Disorder
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Arns, M., Vollebregt, M. A., Palmer, D., Spooner, C., Gordon, E., Kohn, M., . . . Buitelaar, J. K. (2018). Electroencephalographic biomarkers as predictors of methylphenidate response in attention-deficit/hyperactivity disorder. European Neuropsychopharmacology. doi:https://doi.org/10.1016/j.euroneuro.2018.06.002
van der Vinne, N., Vollebregt, M. A., van Putten, M. J., & Arns, M. (2017). Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction? A meta-analysis. NeuroImage: Clinical.
Iseger, T. A., Korgaonkar, M. S., Kenemans, J. L., Grieve, S. M., Baeken, C., Fitzgerald, P. B., & Arns, M. (2017). EEG connectivity between the subgenual anterior cingulate and prefrontal cortices in response to antidepressant medication. European Neuropsychopharmacology; doi:10.1016/j.euroneuro.2017.02.002
Arns, M., Gordon, E., & Boutros, N. N. (2015). EEG abnormalities are associated with poorer depressive symptom outcomes with escitalopram and venlafaxine-xr, but not sertraline: Results from the multicenter randomized iSPOT-D study. Clinical EEG and Neuroscience. doi:10.1177/1550059415621435
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Arns, M., Etkin, A., Hegerl, U., Williams, L.M., DeBattista, C., Palmer, D.M., Fitzgerald, P.B., Harris, A., deBeuss, R. & Gordon, E. (In Press) Frontal and rostral anterior cingulate (rACC) theta EEG in depression: Implications for treatment outcome? European Neuropsychopharmacology.
van Dinteren, R., Arns, M., Kenemans, L., Jongsma, M. L., Kessels, R. P., Fitzgerald, P., . . . Williams, L. M. (2015). Utility of event-related potentials in predicting antidepressant treatment response: An iSPOT-D report. European Neuropsychopharmacology. doi:10.1016/j.euroneuro.2015.07.022
Arns, M., Bruder, G., Hegerl, U., Spooner, C., Palmer, D., Etkin, A., Fallahpour, K., Gatt, J., Hirshberg, L., Gordon, E. (2015). EEG alpha asymmetry as a gender-specific predictor of outcome to acute treatment with different antidepressant medications in the randomized iSPOT-D study. Clinical Neurophysiology 127(1), 509-19. https://dx.doi.org/10.1016/j.clinph.2015.05.032